CN116782827A

CN116782827A - Microneedle devices and methods and skin condition determination

Info

Publication number: CN116782827A
Application number: CN202180091307.5A
Authority: CN
Inventors: 图斌·迪克森; 布拉德福德·塔夫特; 李炳因
Original assignee: Mindera Corp
Current assignee: Mindera Health
Priority date: 2020-11-30
Filing date: 2021-11-29
Publication date: 2023-09-19
Also published as: CA3199737A1; EP4240238A1; KR20230165746A; AU2021385427A1; US20230383352A1; WO2022115714A1; IL303198A; JP2023551537A; EP4240238A4

Abstract

The present disclosure provides microneedle devices and kits for extracting biomarkers from tissue. The present disclosure provides methods of selecting a treatment for an autoimmune disease. The present disclosure also provides nucleic acid analysis methods that prospectively predict a patient's response to a therapeutic agent.

Description

Microneedle devices and methods and skin condition determination

Cross reference

The present application claims priority from U.S. provisional application No. 63/119,511, filed 11/30 in 2020, the contents of which are incorporated herein by reference in their entirety.

Background

Approximately 4% of the world's population is affected by one of more than 80 known autoimmune diseases. In the united states alone, 2400 ten thousand people suffer from autoimmune diseases. It is estimated that the cost of treating autoimmune disease annually in the united states exceeds $ 1000 billion. Autoimmune diseases can encompass a variety of different diseases and conditions that may affect a single tissue or organ, or affect many tissues or organs simultaneously. Some autoimmune diseases are specific to the skin. While the etiology of any particular autoimmune disease is often unknown, imbalance in immune regulation is often involved.

Psoriasis, for example, is a chronic autoimmune disease characterized by abnormal doming of areas of the skin. This is a chronic condition that may lead to the formation of thick, scaly patches or plaques on the skin. It is estimated that over 800 tens of thousands of people in the united states have psoriasis. At present, although there is a treatment available for controlling symptoms, psoriasis has no cure.

Microneedle devices include arrays of relatively small structures, sometimes referred to as microneedles or microneedles (micro-pins), which may be used in conjunction with the delivery of therapeutic agents and other substances through the skin and other surfaces. Although there are treatments for autoimmune diseases, the patient response is different. New strategies are needed to determine the likelihood that a subject with an autoimmune disease will respond to a therapeutic drug.

To treat autoimmune diseases in a subject, physicians may prescribe a therapeutic agent that aims to inhibit or block the biochemical pathways of a particular immune system to manage symptoms. Unfortunately, due to the genetic variability of subjects with autoimmune diseases, a single therapeutic agent may not be an ideal candidate for two subjects with similar autoimmune diseases. This variability between genetic profiles of subjects creates a trial and error approach for prescribing therapeutic agents, often requiring several months to administer therapeutic agents that may prove ineffective for treating a given subject. This approach is both expensive for the insurance payer and an ineffective way to manage autoimmune diseases, potentially having a significant impact on the quality of life of the subject.

Disclosure of Invention

An aspect of the present disclosure provides a microneedle device comprising: 1) A microneedle zone comprising (i) a microneedle base substrate comprising a first base substrate surface and a second base substrate surface, wherein the first base substrate surface and the second base substrate surface are positioned on opposite sides of the microneedle base substrate; and (ii) a plurality of microneedles protruding from the first base substrate surface; and b) a support substrate adjacent to the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate and having a support substrate depth, wherein the support substrate depth is greater than a minimum distance between the first base substrate surface and the second base substrate surface.

Another aspect of the present disclosure provides a microneedle device comprising: 1) A microneedle zone comprising a microneedle base substrate comprising a first base substrate surface having a plurality of microneedles protruding from the first base substrate surface, the microneedle base substrate further comprising a second base substrate surface on an opposite side from the first base substrate surface, the second base substrate surface comprising grooves aligned with at least a portion of the plurality of microneedles; and b) a support substrate adjacent to the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate.

In some embodiments, the minimum distance between the first base substrate surface and the second base substrate surface of the microneedle base substrate is between about 1 μm to about 500 μm less than the support substrate depth. In some embodiments, the minimum distance between the first base substrate surface and the second base substrate surface is between about 150 μm and about 350 μm. In some embodiments, the ratio between (a) the minimum distance between the first base substrate surface and the second base substrate surface and (b) the support substrate depth is at least 1:5. In some embodiments, the microneedle zone comprises an outer perimeter and the support base is adjacent to at least half of the outer perimeter. In some embodiments, the support substrate comprises a first support substrate surface proximal to the plurality of microneedles (sometimes referred to herein as the "front surface of the support substrate surface") and a second support substrate surface distal to the plurality of microneedles (sometimes referred to herein as the "back surface of the support substrate surface") and positioned opposite the first support substrate surface, and wherein the second base substrate surface is not coplanar with a second support substrate surface distal to the plurality of microneedles. In some embodiments, the first base substrate surface is not coplanar with the surface of the support substrate. In some embodiments, the plurality of microneedles are plasma treated. In some embodiments, a plurality of probes is coupled to the microneedles of the plurality of microneedles. In some embodiments, the plurality of probes comprises a negative charge. In some embodiments, the plurality of microneedles comprise a polyolefin resin. In some embodiments, the polyolefin resin comprises one or both of Zeonor 1020R or Zeonor 690R. In some embodiments, the microneedles of the plurality of microneedles are insoluble. In some embodiments, the microneedles of the plurality of microneedles are pyramidal. In some embodiments, the microneedles of the plurality of microneedles are solid. In some embodiments, the angle between the base of the microneedle and the microneedle base is between about 60 ° and about 90 °. In some embodiments, the grooves aligned with at least a portion of the plurality of microneedles have a width greater than the width of the mechanical applicator.

Another aspect of the present disclosure provides a kit comprising a microneedle device, comprising: 1) A microneedle zone comprising a microneedle base substrate comprising a first base substrate surface having a plurality of microneedles protruding from the first base substrate surface, the microneedle base substrate further comprising a second base substrate surface on an opposite side from the first base substrate surface, the second base substrate surface comprising grooves aligned with at least a portion of the plurality of microneedles; and b) a support substrate adjacent to the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate; and (c) a mechanical applicator that fits within a groove aligned with at least a portion of the plurality of microneedles.

In some embodiments, at least one of the plurality of microneedles is coupled to a nucleic acid probe. In some embodiments, the nucleic acid probe comprises a homopolymer sequence. In some embodiments, the homopolymer sequence comprises thymine or uracil. In some embodiments, the nucleic acid probe comprises DNA. In some embodiments, the nucleic acid probe comprises thymine. In some embodiments, the nucleic acid probe comprises thymidine. In some embodiments, the nucleic acid probe is covalently linked to the microneedle. In some embodiments, the support substrate comprises a fiducial marker. In some embodiments, no more than three microneedles of the plurality of microneedles are less than 600 μm in length or greater than 1050 μm in length.

Another aspect of the present disclosure provides a method of preparing a biological sample from a subject, comprising: contacting the skin of the subject with any of the microneedle devices described herein; applying pressure to the microneedle device such that the microneedle device penetrates the skin of the subject; and contacting nucleic acid within the skin of the subject with the microneedle device. In some embodiments, the method further comprises extracting nucleic acids, including, for example, RNA, mRNA. In some embodiments, the method further comprises converting the mRNA to cDNA. In some embodiments, the subject is a human. In some embodiments, the subject has psoriasis or has symptoms of psoriasis. In some embodiments, the subject has a skin condition or has symptoms of a skin condition.

Another aspect of the present disclosure provides a method of preparing a biological sample from a subject, comprising: a) Contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises a plurality of nucleic acid probes coupled to a microneedle; b) Applying pressure to the microneedle device such that the microneedle device penetrates the skin of the subject; c) Allowing the microneedle device to penetrate the skin of the subject for no more than 10 minutes to obtain a ribonucleic acid (RNA) sample hybridized to the nucleic acid probe, wherein the RNA sample comprises a population of RNA fragments having about 700 bases or more and a population of RNA fragments having about 150 bases to about 200 bases, and wherein the ratio of the population of RNA fragments having about 700 bases or more to the population of RNA fragments having about 150 bases to about 200 bases is greater than 1; and d) removing the microneedle device from the skin of the subject, the microneedle device comprising the RNA sample hybridized to the nucleic acid probe after removing the microneedle device from the skin of the subject. In some embodiments, the RNA sample is substantially free of contaminants. In some embodiments, the microneedle device is allowed to penetrate the skin of the subject for about 5 minutes. In some embodiments, the microneedle device penetrates the skin of the subject for less than 60 minutes, less than 45 minutes, less than 40 minutes, less than 30 minutes, less than 15 minutes, less than 10 minutes, less than 5 minutes, less than 3 minutes, less than 2 minutes, or less than 1 minute. In some embodiments, the microneedle device penetrates the skin of the subject for greater than 30 seconds, greater than 45 seconds, greater than 1 minute, greater than 5 minutes, greater than 10 minutes, or greater than 30 minutes.

Another aspect of the present disclosure includes a method of treating a subject having an autoimmune skin disorder, comprising: a) Collecting a sample comprising RNA derived from skin from the subject, wherein the subject has not been administered an autoimmune therapeutic drug within 7 days prior to collecting the sample comprising RNA; b) Determining the expression level of at least one gene based on the RNA; c) Predicting that a subject suffering from said autoimmune skin disease will respond to said autoimmune therapeutic drug with a positive predictive value of greater than 80% based on said expression level of said at least one gene; and d) treating the subject with the autoimmune therapeutic drug based on the prediction in (c). In some cases, the PPV is greater than 90%. In some cases, the PPV is greater than 95% for a cohort with more than 100 patients. In some cases, the autoimmune therapeutic is a biologic or comprises an antibody. In some cases, the autoimmune therapeutic agent is an IL-17 mediated therapy, an IL-23 mediated therapy, or a TNFa mediated therapy. In some cases, the autoimmune therapeutic agent is at least one agent selected from the group consisting of: etanercept, infliximab, adalimumab (adalimumab), certolizumab (certolizumab), wu Sinu mab (ustekinumab), secukinumab (secukinumab), iximab Bei Shan mab (ixekizumab), bromodabigamab (brodalumab), gulekumab (guselkumab), t Qu Jizhu mab (tidrakizumab) and Li Sanji mab (risenkizumab). In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, or five genes. In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, five genes, six genes, seven genes, eight genes, nine genes, or ten genes from table 6, table 12, or table 13. In some cases, the at least one gene comprises at least two genes that do not share a common upstream regulator. In some cases, the autoimmune disorder is psoriasis. In some cases, the object has a PASI of greater than 8. In some cases, the subject has a PASI of at least 75 after treatment of the subject with the autoimmune therapeutic agent. In some cases, the RNA includes mRNA or microrna and is converted to cDNA and then sequenced using next generation sequencing. In some cases, collecting a sample comprising RNA derived from skin from the subject comprises penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises a microneedle conjugated with a nucleic acid probe. In some embodiments, a trained algorithm is applied to the data generated by next generation sequencing of the cDNA. In some embodiments, the algorithm is trained using samples from patients administered a single type of drug selected from the group consisting of: IL-17 mediated therapy, TNF- α mediated therapy, and IL-23 mediated therapy. In some embodiments, the autoimmune therapeutic is an IL-17 mediated therapy, and the at least one gene comprises at least one gene that is not involved in an IL-17 mediated pathway. In some embodiments, the autoimmune therapeutic is an IL-23 mediated therapy, and the at least one gene comprises at least one gene that is not involved in an IL-23 mediated pathway. In some embodiments, the autoimmune therapeutic is a TNF- α mediated therapy and the at least one gene comprises at least one gene that is not involved in a TNF- α mediated pathway.

Another aspect of the present disclosure includes a method of determining whether a skin lesion of a subject will respond to an autoimmune therapeutic drug, comprising: collecting a sample comprising RNA derived from skin from the subject, wherein the subject has not administered the autoimmune therapeutic drug within 7 days prior to collecting the sample comprising RNA; converting the RNA to cDNA; determining the expression of at least one gene based on the cDNA; and predicting with a positive predictive value of greater than 80% whether the subject with the skin lesion will respond to the autoimmune therapeutic drug. In some cases, the PPV is greater than 90%. In some cases, the PPV is greater than 95% for a cohort with more than 100 patients. In some embodiments, the autoimmune therapeutic is a biologic or comprises an antibody. In some embodiments, the autoimmune therapeutic agent is an IL-17 mediated therapy, an IL-23 mediated therapy, or a TNFa mediated therapy. In some embodiments, the autoimmune therapeutic agent is at least one agent selected from the group consisting of: etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan mab, budodamab, antique-in-c-lib mab, ti Qu Jizhu mab and Li Sanji mab. In some embodiments, the autoimmune therapeutic agent is at least one agent selected from the group consisting of: cetuximab, wu Sinu mab, secukinumab, iral Bei Shan mab, brodamab, guluromab, ti Qu Jizhu mab and Li Sanji mab. In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, or five genes. In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, five genes, six genes, seven genes, eight genes, nine genes, or ten genes from table 6, table 12, or table 13. In some cases, the at least one gene comprises at least two genes that do not share a common upstream regulator. In some cases, the autoimmune disorder is psoriasis. In some cases, the object has a PASI of greater than 8. In some cases, the subject has a PASI of at least 75 after treatment of the subject with the autoimmune therapeutic agent. In some cases, the RNA includes mRNA or microrna and is converted to cDNA and then sequenced using next generation sequencing. In some cases, collecting a sample comprising RNA derived from skin from the subject comprises penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises a microneedle conjugated with a nucleic acid probe. In some embodiments, a trained algorithm is applied to the data generated by next generation sequencing of the cDNA. In some embodiments, the algorithm is trained using samples from patients administered a single type of drug selected from the group consisting of: IL-17 mediated therapy, TNF- α mediated therapy, and IL-23 mediated therapy.

Another aspect of the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an autoimmune therapeutic drug, comprising: penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a microneedle; removing the microneedle device from the skin of the subject, thereby obtaining RNA molecules from the subject; high throughput sequencing of the RNA molecules to generate sequence reads; aligning the sequence reads with a sequence read signature (signature) associated with a positive response to an autoimmune disease therapeutic drug to obtain aligned sequence reads; and applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 80% for predicting a response to the autoimmune disease therapeutic drug.

Another aspect of the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an autoimmune therapeutic drug, comprising: penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a solid microneedle; removing the microneedle device from the skin of the subject, thereby obtaining RNA molecules from the subject; high throughput sequencing of the RNA molecules to generate sequence reads; and aligning the sequence reads with sequence read signatures associated with positive responses to autoimmune disease treatment drugs to obtain aligned sequence reads; determining the expression level of at least one RNA molecule using the aligned sequence reads; and applying a trained algorithm to the expression level of the at least one RNA molecule, wherein the trained algorithm predicts whether the subject with the skin lesion will respond to IL-17 mediated therapy, IL-23 mediated therapy, tnfa mediated therapy, or any combination thereof. In some embodiments, the subject will respond to the IL-17 mediated therapy, the IL-23 mediated therapy, and the TNFα mediated therapy. In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, or five genes. In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, five genes, six genes, seven genes, eight genes, nine genes, or ten genes from table 6, table 12, or table 13. In some cases, the at least one gene comprises at least two genes that do not share a common upstream regulator. In some cases, the autoimmune disorder is psoriasis. In some cases, the object has a PASI of greater than 8. In some cases, the subject has a PASI of at least 75 after treatment of the subject with the autoimmune therapeutic agent. In some cases, the RNA includes mRNA or microrna and is converted to cDNA and then sequenced using next generation sequencing. In some embodiments, the recommended treatment includes one or more autoimmune therapeutic drugs against an autoimmune disease or condition. In some embodiments, the recommended treatment includes etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan antibody, buddamab, gulkumab, tem Qu Jizhu mab, li Sanji bead mab, or any combination thereof.

In some embodiments, the methods described herein further comprise high throughput sequencing the RNA biomarker to generate one or more sequence reads of the subject; aligning the one or more sequence reads of the subject with a known sequence read signature, wherein the known sequence read signature is associated with a positive response to the recommended treatment, thereby obtaining aligned sequence reads; and classifying the subject as having a likelihood of a positive response to the recommended treatment by applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 50% for predicting a positive response to the recommended treatment. In some embodiments, the trained algorithm has a negative predictive value of greater than 50%.

Another aspect of the present disclosure provides a method of determining whether a subject suffering from an autoimmune skin disorder will respond to an autoimmune therapeutic drug, comprising: extracting mRNA from the skin of the subject; sequencing the mRNA from the skin of the subject; and predicting with a positive predictive value of greater than 80% whether the subject having the autoimmune disorder will respond to etanercept, adalimumab, infliximab, cetuximab, secuzumab, exenatide Bei Shan, buddamab, coumarone, ti Qu Jizhu mab, and Li Sanji bead mab.

Another aspect of the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an IL-23 mediated therapy, comprising: contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a solid microneedle such that the microneedle device penetrates the skin of the subject; removing the microneedle device from the skin of the subject, thereby obtaining RNA molecules from the subject; high throughput sequencing of the RNA molecules to generate sequence reads; and aligning the sequence reads with sequence read signatures associated with positive responses to autoimmune disease treatment drugs to obtain aligned sequence reads; and applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm predicts whether the subject with the skin lesion will respond to IL-23 mediated therapy, and the aligned sequence reads correspond to at least one gene from table 13.

Another aspect of the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an IL-17, IL-23 or TNF- α mediated treatment, comprising: contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a solid microneedle such that the microneedle device penetrates the skin of the subject; removing the microneedle device from the skin of the subject, thereby obtaining RNA molecules from the subject; high throughput sequencing of the RNA molecules to generate sequence reads; and aligning the sequence reads with sequence read signatures associated with positive responses to autoimmune disease treatment drugs to obtain aligned sequence reads; and applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm predicts whether the subject with the skin lesion will respond to IL-17 mediated therapy, TNF- α mediated therapy, or IL-23 mediated therapy, and the aligned sequence reads correspond to at least one gene from table 6, table 12, or table 13. In some embodiments, the subject is treated with an IL-17 mediated therapy, wherein the aligned sequence reads correspond to at least one gene, two genes, three genes, four genes, five genes, or six genes from table 12. In some embodiments, the subject is treated with TNF- α mediated therapy, wherein the aligned sequence reads correspond to at least one gene, two genes, three genes, four genes, five genes, or six genes from table 6. In some embodiments, the subject is treated with an IL-23 mediated therapy, wherein the aligned sequence reads correspond to at least one gene, two genes, three genes, four genes, five genes, or six genes from table 13.

In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: CNFN, CTSC, GBAP1, CRABP2, PCDH7, PPIG, RAB31, C3 and EGR. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: CNFN, CTSC, GBAP1, CRABP2, PCDH7 and PPIG. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: PCDH7, PPIG, RAB31, C3 and EGR.

In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: CNFN, CTSC, GBAP1 and CRABP2. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: PPIG, RAB31, C3 and EGR. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: PCDH7, PPIG, RAB31 and C3. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: GBAP1, CRABP2, PCDH7, PPIG.

In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: SERPINB3, SERPINB4, S100A7A, PI3, KRT6A, LCN2, DEFB4A, DEFB4B, SPRR1A, IL36G, MX, IFI27, CD36, CD24, and IL4R. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: KRT6A, SPRR1A, CD, IL4R, LCN2 and IFI27. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: CD36, IL4R, S100A7A, SERPINB4, MX1 and SERPINB3. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: LCN2, IFI27, DEFB4A, IL36G, CD, and PI3.

In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: IL4R, LCN and IFI27. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: PI3, IFI27 and SERPINB3. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: IL4R, S A7A and MX1. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: CD36, LCN2, and SERPINB4.

In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: MTCO1P12, MTATP6P1, CLSTN1, PDPN, LDLRAD2, and GSTM3. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: AL158847.1, DAD1, LDLRAD2, ZNF395, MGMT and AL136982.4. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: NREP, PPIF, PRIM1, AL136982.5, MTATP6P1 and SMPD3. In some cases, the at least one gene is at least one gene, two genes, three genes, four genes, five genes, or six genes selected from the group consisting of: PDPN, TXNRD1, GSTM3, GPSM1, GLRX, and USP2.

In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: MTCO1P12, CLSTN1 and GSTM3. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: NREP, PPIF and PRIM1. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: AL136982.5, MTATP6P1 and SMPD3. In some cases, the at least one gene is at least one gene, two genes, or three genes selected from the group consisting of: PDPN, TXNRD1, and GSTM3.

Also provided herein are microneedle devices, methods, and systems for characterizing autoimmune diseases in a subject and the likelihood that a subject may produce a positive response to a given therapeutic agent to treat the autoimmune disease in the subject.

One aspect of the present disclosure includes a microneedle device comprising: a microneedle zone comprising (i) a plurality of microneedles protruding from a front surface of a microneedle base substrate, (ii) a rear surface of the microneedle base substrate, and (iii) a minimum distance between the front surface of the microneedle base substrate and the rear surface of the microneedle base substrate; and a support substrate adjacent to at least one side of the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate and having a support substrate depth, wherein the support substrate depth is greater than the minimum distance between the front surface of the microneedle base substrate and the rear surface of the microneedle base substrate.

Another aspect of the present disclosure includes a microneedle device comprising: a microneedle zone comprising a plurality of microneedles protruding from a front surface of a microneedle base substrate, the microneedle base substrate further comprising a rear surface comprising a recess directly behind at least a portion of the microneedle zone; and a support substrate adjacent to at least one side of the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate. In some embodiments, the minimum distance between the front surface of the microneedle base substrate and the rear surface of the microneedle base substrate is between about 1 μm to about 500 μm less than the depth of the support substrate. In some embodiments, the minimum distance between the front surface of the microneedle base substrate and the rear surface of the microneedle base substrate is between 150 μιη to about 350 μιη. In some embodiments, the minimum distance between the front surface of the microneedle base substrate and the rear surface of the microneedle base substrate and the support substrate depth have a ratio of about 1:5. In some embodiments, the minimum distance between the front surface of the microneedle base substrate and the back surface of the microneedle base substrate and the support substrate depth have a ratio of at least about 0.1:5, a ratio of at least about 0.5:5, a ratio of at least about 1:5, a ratio of at least about 2:5, a ratio of at least 3:5, a ratio of at least 4:5, a ratio of at least 1:1, a ratio of at least 1:2, a ratio of at least 1:3, a ratio of at least 1:4, a ratio of at least about 1:10, a ratio of at least about 1:15, a ratio of at least about 1:20, a ratio of at least about 1:25, or a ratio of at least about 1:50. In some embodiments, the minimum distance between the front surface of the microneedle base substrate and the back surface of the microneedle base substrate and the support substrate depth have a ratio of at most about 0.1:5, a ratio of at most about 0.5:5, a ratio of at most about 1:5, a ratio of at most about 2:5, a ratio of at most 3:5, a ratio of at most 4:5, a ratio of at most 1:1, a ratio of at most 1:2, a ratio of at most 1:3, a ratio of at most 1:4, a ratio of at most about 1:10, a ratio of at most about 1:15, a ratio of at most about 1:20, a ratio of at most about 1:25, or a ratio of at most about 1:50. In some embodiments, the minimum distance between the front surface of the microneedle base substrate and the back surface of the microneedle base substrate and the support substrate depth have a ratio of at least about 2:1, a ratio of at least about 3:1, a ratio of at least about 3:2, a ratio of at least about 4:1, a ratio of at least 5:1, a ratio of at least 5:3, a ratio of at least 6:1, a ratio of at least 7:1, a ratio of at least 10:1, a ratio of at least 15:1, a ratio of at least about 20:1, a ratio of at least about 25:1, or a ratio of at least about 50:1. In some embodiments, the minimum distance between the front surface of the microneedle base substrate and the back surface of the microneedle base substrate and the support substrate depth have a ratio of at most about 2:1, a ratio of at most about 3:1, a ratio of at most about 3:2, a ratio of at most about 4:1, a ratio of at most 5:1, a ratio of at most 5:3, a ratio of at most about 6:1, a ratio of at most 7:1, a ratio of at most 10:1, a ratio of at most 15:1, a ratio of at most about 20:1, a ratio of at most about 25:1, or a ratio of at most about 50:1. In some embodiments, the microneedle zone comprises an outer perimeter and the support base is adjacent to at least half of the outer perimeter. In some embodiments, the rear surface of the microneedle base substrate is not coplanar with a rear surface of the support substrate. In some embodiments, the front surface of the microneedle base substrate is not coplanar with a surface of the support substrate. In some embodiments, the plurality of microneedles are plasma treated. In some embodiments, a plurality of probes is coupled to the microneedles of the plurality of microneedles. In some embodiments, the plurality of probes comprises a negative charge. In some embodiments, the plurality of microneedles comprise a polyolefin resin. In some embodiments, the polyolefin resin comprises one or both of Zeonor 1020R or Zeonor 690R. In some embodiments, the microneedles of the plurality of microneedles are insoluble. In some embodiments, the microneedles of the plurality of microneedles are pyramidal. In some embodiments, the microneedles of the plurality of microneedles are solid. In some embodiments, the angle between the base of the microneedle and the microneedle base is between about 60 ° and about 90 °. In some embodiments, the groove directly behind the at least a portion of the microneedle area has a width that is greater than a width of a mechanical applicator.

Another aspect of the disclosure includes a kit comprising: (a) the apparatus of some aspects described herein; and (b) a mechanical applicator that fits within the recess immediately behind the at least a portion of the microneedle area. In some embodiments, at least one of the plurality of microneedles is coupled to a nucleic acid probe. In some embodiments, the nucleic acid probe comprises a homopolymer sequence. In some embodiments, the homopolymer sequence comprises thymine or uracil. In some embodiments, the nucleic acid probe is covalently linked to the microneedle. In some embodiments, the support substrate comprises a fiducial marker. In some embodiments, no more than three microneedles of the plurality of microneedles are less than 600 μm in length or greater than 1050 μm in length.

Another aspect of the present disclosure includes a method of preparing a biological sample from a subject, comprising: contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises a plurality of nucleic acid probes coupled to a microneedle; applying pressure to the microneedle device such that the microneedle device penetrates the skin of the subject; allowing the microneedle device to penetrate the skin of the subject for no more than 10 minutes to obtain a ribonucleic acid (RNA) sample hybridized to the nucleic acid probe, wherein the RNA sample comprises a population of RNA fragments having about 700 bases or more and a population of RNA fragments having about 150 bases to about 200 bases, and wherein the ratio of the population of RNA fragments having about 700 bases or more to the population of RNA fragments having about 150 bases to about 200 bases is greater than 1; and removing the microneedle device from the skin of the subject, the microneedle device comprising the RNA sample hybridized to the nucleic acid probe after removing the microneedle device from the skin of the subject. In some embodiments, the RNA sample is substantially free of contaminants. In some embodiments, the method comprises penetrating the microneedle device through the skin of the subject for about 5 minutes.

Another aspect of the present disclosure includes a method of predicting an individual patient's response to a class of agents by the mechanism of action of a class of biopharmaceuticals to optimize treatment options. By using a machine learning approach, training the classifier uses baseline transcriptome biomarkers (e.g., certain genes) and predicts the patient's response to biologicals of the IL-17, IL-23 or TNF class with high positive predictive values, e.g., by using a machine learning derived classifier to predict the response to anti-IL-17 and anti-IL-23 biologicals in patients with psoriasis with high levels of positive predictive values.

Another aspect of the disclosure includes an algorithm for predicting a response to a biologic (e.g., anti-IL-17, anti-IL-23) for treating a patient with psoriasis by comparing a baseline transcriptome to a clinical response to the biologic after an initial treatment, e.g., after 12 weeks.

Another aspect of the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an autoimmune therapeutic drug, comprising: penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a microneedle; removing the microneedle device from the skin of the subject, thereby obtaining RNA molecules from the subject; high throughput sequencing of the RNA molecules to generate sequence reads; quantifying an RNA molecule comprising a sequence read associated with a positive response to an autoimmune disease therapeutic drug to determine the expression level of at least one gene; and applying a trained algorithm to the expression level, wherein the trained algorithm has a positive predictive value of greater than 80% for predicting a response to the autoimmune disease therapeutic drug.

Another aspect of the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an autoimmune therapeutic drug, comprising: penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a solid microneedle; removing the microneedle device from the skin of the subject, thereby obtaining RNA molecules from the subject; high throughput sequencing of the RNA molecules to generate sequence reads; and quantifying an RNA molecule comprising a sequence read associated with a positive response to the autoimmune disease therapeutic agent to determine the expression level of the at least one gene; and applying a trained algorithm to the expression level of the at least one RNA molecule, wherein the trained algorithm predicts whether the subject with the skin lesion will respond to IL-17 mediated therapy, IL-23 mediated therapy, tnfa mediated therapy, or any combination thereof. In some embodiments, the subject will respond to the IL-17 mediated therapy, the IL-23 mediated therapy, and the TNFα mediated therapy. In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, or five genes. In some embodiments, the at least one gene comprises at least two genes, three genes, four genes, five genes, six genes, seven genes, eight genes, nine genes, or ten genes from table 6, table 12, or table 13. In some cases, the at least one gene comprises at least two genes that do not share a common upstream regulator. In some cases, the autoimmune disorder is psoriasis. In some cases, the object has a PASI of greater than 8. In some cases, the subject has a PASI of at least 75 after treatment of the subject with the autoimmune therapeutic agent. In some cases, the RNA includes mRNA or microrna and is converted to cDNA and then sequenced using next generation sequencing. In some embodiments, the recommended treatment includes one or more autoimmune therapeutic drugs against an autoimmune disease or condition. In some embodiments, the recommended treatment includes etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan antibody, buddamab, gulkumab, tem Qu Jizhu mab, li Sanji bead mab, or any combination thereof.

In some embodiments, the methods described herein further comprise high throughput sequencing the RNA biomarker to generate one or more sequence reads of the subject; quantifying an RNA molecule of the subject comprising one or more sequence reads with a known sequence read signature, wherein the known sequence read signature is associated with a positive response to the recommended treatment, thereby obtaining an aligned sequence read; and classifying the subject as having a likelihood of producing a positive response to the recommended treatment by applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 50% for predicting a positive response to the recommended treatment. In some embodiments, the trained algorithm has a negative predictive value of greater than 50%.

Incorporation by reference

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

Drawings

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

Fig. 1 illustrates a cross-section of a microneedle device as described herein.

Fig. 2 illustrates a detail of the section from fig. 1.

Fig. 3 illustrates a front view of a microneedle device as described herein.

Fig. 4 illustrates a microneedle of a microneedle device as described herein.

Fig. 5A-5C illustrate the mass of different RNA samples collected with a microneedle device as described herein, measured as the length of the RNA fragments contained in the sample. Figure 5A illustrates an undegraded sample. Fig. 5B and 5C illustrate degraded samples.

Fig. 6A illustrates an exemplary flow chart of a method of treating one or more subjects having a mild or severe form of skin condition, as described in some embodiments herein.

Fig. 6B illustrates an exemplary flow chart of a method of preparing a biological sample and identifying one or more therapeutic agents for one or more subjects, as described in some embodiments herein.

Fig. 6C illustrates an exemplary flow chart for identifying activation of one or more pathways that may be associated with a disease or condition of a subject.

Figure 7 shows a flow chart of sample recruitment and analysis for study patients.

Figures 8A-8C show the correlation between predicted responses from psoriasis patients from independent validation data sets and patient week 12 PASI changes. Fig. 8A:43 patients treated with IL-23 i; fig. 8B:31 patients treated with IL-17 i; fig. 8C:11 patients treated with TNFαi. X axis: predicting respondents or non-respondents; y axis: PASI change at week 12. Red dot: median PASI change value.

Figure 9 shows predicted response incidence for patients enrolled in the study.

FIG. 10 shows an overview of RNA-Seq analysis.

Fig. 11 compares lesion and non-lesion samples with a puncture biopsy using the Mindera patch from 66 patients in the Mindera database.

FIG. 12 shows a heat map generated from expression data of 17 genes identified as being correlated with response to anti-IL-17 biotherapy.

Detailed Description

SUMMARY

Described herein are microneedle devices having features that enable accurate, patient-friendly sample collection, including microneedle devices having features that can minimize pain during sample collection. The devices described herein may be specifically designed so that they can be manufactured by scalable processes. Features such as different thicknesses of different regions of the devices described herein may result in uniform and accurate microneedle devices. The particular features of the microneedle devices described herein may also result in sharper microneedles that are not described in the state of the art. These sharp microneedles may result in an enhanced user or patient (e.g., subject) experience in which the user may experience significantly less pain than state-of-the-art microneedle devices. Combining the precision of the microneedle device with an enhanced user experience allows for more accurate and precise analysis of biological samples (e.g., the skin of a user).

Generally, the microneedle devices provided herein can contain probes attached to microneedles. In some cases, these probes are configured to bind to one or more biomarkers within a sample or tissue (e.g., skin) of a subject, so as to allow the biomarkers to be extracted for further analysis. The extracted biomarkers can be analyzed (e.g., to generate a genetic signature or gene expression profile) alone or in combination to provide a diagnosis or prediction of the subject's response to a drug or other treatment.

Also provided herein are methods and systems for determining a gene expression profile from a subject. In some cases, the gene expression profile can be used to determine an appropriate treatment regimen for an autoimmune disease. Thus, the methods of the present disclosure are useful for personalized medicine, where the treatment is tailored to the subject based on the specific characteristics of the subject's disease.

In some embodiments, the present disclosure provides a microneedle device. In some embodiments, the microneedle device comprises a microneedle zone and a support base. The microneedle area may include a plurality of microneedles 110 protruding from a front or first surface of the microneedle base substrate; a rear surface (or second base substrate surface) 160 of the microneedle base substrate; and a minimum distance 170 between the front surface of the microneedle base substrate and the rear surface of the microneedle base substrate. In some embodiments, the minimum distance 170 between the front surface of the microneedle base substrate and the back surface of the microneedle base substrate configures the device such that during some methods of manufacturing the device, the molten medium (e.g., the polyolefin resin described herein) will fill the mold of the microneedle before filling the mold of the other portion of the device. In some embodiments, the minimum distance 170 between the front surface of the microneedle base substrate and the back surface of the microneedle base substrate configures the device such that the device comprises uniform and/or sharp microneedles. In some embodiments, the uniform and/or sharp microneedles create an enhanced user experience for a user using the device. In some embodiments, the enhanced user experience includes a reduction in pain experienced by a user using the device.

The present disclosure provides microneedle devices for in situ detection and acquisition of biomarkers from a subject. Microneedle-based devices may contain one or more microneedles that may penetrate into a biological barrier such as the skin or mucosa. Microneedles are often non-invasive or minimally invasive. When there are multiple microneedles, the device may also have a planar base that supports the microneedles. The substrate may be made of the same material as the microneedles. It can also be made of different materials.

Microneedle device

The microneedle devices of the present disclosure may include a base substrate having a microneedle zone and a support substrate. Fig. 1 illustrates a side view of an exemplary microneedle device 100 comprising microneedles 110 on a microneedle base substrate 120. The microneedle base substrate includes a front surface (used interchangeably herein with the term "first base substrate surface") 140, with the microneedles 110 protruding from the front surface (or first base substrate surface); and a rear surface (used interchangeably herein with the term "second base substrate surface") 160. The device may include a support substrate 130 adjacent to at least one side of the inner microneedle base substrate 120, which is connected to or integral with the microneedle base substrate. In some embodiments, the rear surface 160 of the microneedle base substrate forms an angle with the adjacent support substrate 130.

In some embodiments, the angle formed between the rear surface 160 of the microneedle base substrate and the adjacent support substrate 130 is a right or obtuse angle, for example, about 90 ° to about 179 °. The angle (θ) formed between the rear surface 160 of the microneedle base substrate and the adjacent support substrate 130 is generally referred to as the angle that spans the spatial region of the interior of the device; while adjacent support substrates typically include a complementary angle (α) that spans the substrate and is often acute (e.g., between 1 ° and 90 °). In some embodiments, the angle (θ) formed between the rear surface of the microneedle base substrate and the adjacent support substrate is about 110 °. In some embodiments, the angle (θ) formed between the back surface of the microneedle base substrate and the adjacent support substrate is about 90 ° to about 100 °, about 90 ° to about 110 °, about 90 ° to about 120 °, about 90 ° to about 130 °, about 90 ° to about 140 °, about 90 ° to about 150 °, about 90 ° to about 160 °, about 90 ° to about 170 °, about 90 ° to about 179 °, about 100 ° to about 110 °, about 100 ° to about 120 °, about 100 ° to about 130 °, about 100 ° to about 140 °, about 100 ° to about 150 °, about 100 ° to about 160 °, about 100 ° to about 170 °, about 100 ° to about 179 °, about 110 ° to about 120 °, about 110 ° to about 130 °, about 110 ° to about 140 °, about 110 ° to about 150 °, about 110 ° to about 160 °, about 110 ° to about 170 °, about 120 ° to about 120 °, about 120 ° to about 150 °, about 120 ° to about 160 °, about 170 ° to about 179 °, about 170 ° to about 170 °, about 170 ° to about 150 °, about 170 ° to about 179 °, about 170 ° to about 150 °, about 170 ° to about 170 °, about 170 ° to about 179 °, about 150 °, about 170 ° to about 150 °, or about 170 ° to about 179 °. In some embodiments, the angle formed between the rear surface of the microneedle base substrate and the adjacent support substrate is about 90 °, about 100 °, about 110 °, about 120 °, about 130 °, about 140 °, about 150 °, about 160 °, about 170 °, or about 179 °. In some embodiments, the angle formed between the rear surface of the microneedle base substrate and the adjacent support substrate is at least about 90 °, about 100 °, about 110 °, about 120 °, about 130 °, about 140 °, about 150 °, about 160 °, or about 170 °. In some embodiments, the angle formed between the rear surface of the microneedle base substrate and the adjacent support substrate is at most about 100 °, about 110 °, about 120 °, about 130 °, about 140 °, about 150 °, about 160 °, about 170 °, or about 179 °.

The resulting shape of support substrate 130 may also affect the rate and uniformity of flow of polymer and/or resin into mold portions similar to the device structure during fabrication of the device. In some embodiments, the resulting shape of support substrate 130 is circular. In some embodiments, the resulting shape of support substrate 130 is linear.

In some cases, the arrangement of microneedles is a circular pattern in which the radius of the microneedle portions is about 0.95mm to about 4.15mm. In some embodiments, the radius is about 4.0mm. In some embodiments, the radius is about 0.95mm to about 1.4mm, about 0.95mm to about 1.75mm, about 0.95mm to about 2.15mm, about 0.95mm to about 2.55mm, about 0.95mm to about 2.95mm, about 0.95mm to about 3.35mm, about 0.95mm to about 3.75mm, about 0.95mm to about 4.15mm, about 1.4mm to about 1.75mm, about 1.4mm to about 2.15mm, about 1.4mm to about 2.55mm, about 1.4mm to about 2.95mm, about 1.4mm to about 3.35mm, about 1.4mm to about 3.75mm, about 1.4mm to about 4.15mm, about 1.75mm to about 2.55mm, about 1.75mm to about 2.95mm, about 1.75mm to about 2.75 mm, about 1.35 mm to about 3.35mm, about 3.3.35 mm to about 2.55mm, about 2.55mm to about 2.55mm, about 2.15mm to about 2.55mm, about 2.55mm to about 2.55mm, about 2.55mm to about 2.55 mm. In some embodiments, the radius is about 0.95mm, about 1.4mm, about 1.75mm, about 2.15mm, about 2.55mm, about 2.95mm, about 3.35mm, about 3.75mm, or about 4.15mm. In some embodiments, the radius is at least about 0.95mm, about 1.4mm, about 1.75mm, about 2.15mm, about 2.55mm, about 2.95mm, about 3.35mm, or about 3.75mm. In some embodiments, the radius is up to about 1.4mm, about 1.75mm, about 2.15mm, about 2.55mm, about 2.95mm, about 3.35mm, about 3.75mm, or about 4.15mm.

In some cases, the arrangement of microneedles is a linear square pattern with a total edge length of about 2.14mm to about 9.34mm. In some embodiments, the distance is about 4.0mm. In some embodiments of the present invention, in some embodiments, the distance is from about 2.14mm to about 3.04mm, from about 2.14mm to about 3.94mm, from about 2.14mm to about 4.84mm, from about 2.14mm to about 5.74mm, from about 2.14mm to about 6.64mm, from about 2.14mm to about 7.54mm, from about 2.14mm to about 8.44mm, from about 2.14mm to about 9.34mm, from about 3.04mm to about 3.94mm, from about 3.04mm to about 4.84mm, from about 3.04mm to about 5.74mm, from about 3.04mm to about 6.64mm, from about 3.04mm to about 7.54mm, from about 3.04mm to about 8.44mm, from about 3.04mm to about 9.34mm, from about 3.94mm to about 4.84mm, from about 3.94mm to about 5.74mm, from about 3.94mm, to about 6.64mm about 3.94mm to about 7.54mm, about 3.94mm to about 8.44mm, about 3.94mm to about 9.34mm, about 4.84mm to about 5.74mm, about 4.84mm to about 6.64mm, about 4.84mm to about 7.54mm, about 4.84mm to about 8.44mm, about 4.84mm to about 9.34mm, about 5.74mm to about 6.64mm, about 5.74mm to about 7.54mm, about 5.74mm to about 8.44mm, about 5.74mm to about 9.34mm, about 6.64mm to about 7.54mm, about 6.64mm to about 8.44mm, about 6.64mm to about 9.34mm, about 7.54mm to about 8.44mm, about 7.54mm to about 9.34mm, or about 8.44mm to about 9.34mm. In some embodiments, the distance is about 2.14mm, about 3.04mm, about 3.94mm, about 4.84mm, about 5.74mm, about 6.64mm, about 7.54mm, about 8.44mm, or about 9.34mm. In some embodiments, the distance is at least about 2.14mm, about 3.04mm, about 3.94mm, about 4.84mm, about 5.74mm, about 6.64mm, about 7.54mm, or about 8.44mm. In some embodiments, the distance is up to about 3.04mm, about 3.94mm, about 4.84mm, about 5.74mm, about 6.64mm, about 7.54mm, about 8.44mm, or about 9.34mm.

In some embodiments, the surface from which the microneedles protrude and are applied to the tissue of interest may be referred to as the bottom surface or the front surface. In some embodiments, the surface 160 opposite the surface from which the microneedles protrude may be referred to as a back surface or top surface.

In some embodiments, the microneedle base substrate 120 is defined by the presence of microneedles. The microneedle base substrate 120 may be thinner and/or narrower than the support substrate 130, for example, the distance between the front surface 140 of the microneedle base substrate and the rear surface 160 of the microneedle base substrate (referred to as D _BS 170 in fig. 1) is less than the distance between respective adjacent surfaces of the support substrate 130 (referred to as D _SS 180 in fig. 1). The depth difference between the microneedle base substrate 120 and the support substrate 130 can create grooves in the device that are positioned adjacent to the microneedle base substrate 120. This structural feature is particularly advantageous because having a narrower microneedle base substrate 120 improves the flow and/or penetration of the polymer/resin used to fabricate the device into the mold similar to the device structure during fabrication, particularly because of this structural feature of the depth differential between the microneedle base substrate 120 and the support substrate 130, the flow and/or penetration of the polymer/resin into the mold portions corresponding to the microneedles increases, resulting in more uniform and sharper microneedles. Sharper microneedles can produce less tissue damage when inserted and can therefore reduce pain in the subject. More uniform microneedles result in a more standard and scalable manufacturing process.

In some embodiments, the narrower microneedle base substrate may also concentrate pressure through the microneedles when the device is applied to the skin or other tissue. In some cases, the minimum distance between the front surface 140 of the microneedle base substrate and the rear surface 150 of the microneedle base substrate is between about 1 μm to about 500 μm, less than the distance of the front surface of the support substrate to the rear surface of the support substrate.

In some embodiments, the microneedle base substrate (D _BS ) And a support base (D) _SS ) The depth difference between them is about 1 μm to about 550 μm. In some embodiments, the microneedle base substrate (D _BS ) And a support base (D) _SS ) The depth difference between them is about 250 μm. In some embodiments, the microneedle base substrate (D _BS ) And a support base (D) _SS ) The depth difference therebetween is about 1 μm to about 50 μm, about 1 μm to about 100 μm, about 1 μm to about 150 μm, about 1 μm to about 200 μm, about 1 μm to about 250 μm, about 1 μm to about 300 μm, about 1 μm to about 350 μm, about 1 μm to about 400 μm, about 1 μm to about 450 μm, about 1 μm to about 500 μm, about 1 μm to about 550 μm, about 50 μm to about 100 μm, about 50 μm to about 150 μm, about 50 μm to about 200 μm, about 50 μm to about 250 μm, about 50 μm to about 300 μm, about 50 μm to about 350 μm, about 50 μm to about 400 μm, about 50 μm to about 450 μm, about 50 μm to about 500 μm, about 50 μm to about 550 μm, about 100 μm to about 150 μm, about 100 μm to about 200 μm, about 200 μm to about 100 μm, about 100 μm to about 300 μm, about 50 μm to about 350 μm about 100 μm to about 400 μm, about 100 μm to about 450 μm, about 100 μm to about 500 μm, about 100 μm to about 550 μm, about 150 μm to about 200 μm, about 150 μm to about 250 μm, about 150 μm to about 300 μm, about 150 μm to about 350 μm, about 150 μm to about 400 μm, about 150 μm to about 450 μm, about 150 μm to about 500 μm, about 150 μm to about 550 μm, about 200 μm to about 250 μm, about 200 μm to about 300 μm, about 200 μm to about 350 μm, about 200 μm to about 400 μm, about 200 μm to about 450 μm, about 200 μm to about 500 μm, about 200 μm to about 550 μm, about 250 μm to about 300 μm, about 250 μm to about 350 μm, about 250 μm to about 400 μm, about 250 μm to about 450 μm, about 250 μm to about 250 μm, about 250 μm to about 500 μm, about 500 μm About 300 μm to about 350 μm, about 300 μm to about 400 μm, about 300 μm to about 450 μm, about 300 μm to about 500 μm, about 300 μm to about 550 μm, about 350 μm to about 400 μm, about 350 μm to about 450 μm, about 350 μm to about 500 μm, about 350 μm to about 550 μm, about 400 μm to about 450 μm, about 400 μm to about 500 μm, about 400 μm to about 550 μm, about 450 μm to about 500 μm, about 450 μm to about 550 μm μm or about 500 μm to about 550 μm. In some embodiments, the depth difference between the microneedle base substrate and the support substrate is about 1 μm, about 50 μm, about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, or about 550 μm. In some embodiments, the microneedle base substrate (D _BS ) And a support base (D) _SS ) The depth difference between them is at least about 1 μm, about 50 μm, about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, or about 500 μm. In some embodiments, the depth difference between the microneedle base substrate and the support substrate is at most about 50 μm, about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, or about 550 μm.

In some cases, the minimum distance (D _BS ) Is between 150 μm and about 350 μm. In some cases, the ratio of the distance between the front and rear of the inner portion and the distance between the front and rear of the outer peripheral portion is a ratio of about 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, or 1:10.

In some cases, the microneedle portion or region comprises a perimeter and the perimeter region or support base is adjacent to at least half of the perimeter. In some cases, the rear surface of the microneedle base substrate is not coplanar with the rear surface of the support substrate. In some cases, the front surface of the microneedle base substrate is not coplanar with the surface of the support substrate.

Fig. 2 provides a detailed view of the circular area of fig. 1. In some embodiments, the inner portion may also have a ridge 200 that surrounds the microneedle area. The ridge may protrude from a surface from which the microneedle protrudes. In some embodiments, height H of the ridge _R (210 in FIG. 2) may be about 250 μm. In some embodiments, height H of the ridge _R May be about 50 μm, 100 μm, 150 μm, 200 μm, 250 μm, 254 μm, 300 μm, 350 μm, 400 μm or 450 μm. In some cases, ridges surrounding the inner portion may augment or maintain tissue (such as skin) when the device is applied to the tissueSurface tension. In some cases, the angle formed between the ridge and the bottom surface is about 5 ° to about 85 °. In some cases, the angle formed between the ridge and the bottom surface is about 45 °. In some of the cases where the number of the cases, the angle formed between the ridge and the bottom surface is about 5 ° to about 10 °, about 5 ° to about 20 °, about 5 ° to about 30 °, about 5 ° to about 40 °, about 5 ° to about 50 °, about 5 ° to about 60 °, about 5 ° to about 70 °, about 5 ° to about 80 °, about 5 ° to about 85 °, about 10 ° to about 20 °, about 10 ° to about 30 °, about 10 ° to about 40 °, about 10 ° to about 50 °, about 10 ° to about 60 °, about 10 ° to about 70 °, about 10 ° to about 80 °, about 10 ° to about 85 °, about 20 ° to about 30 °, about 20 ° to about 40 °, about 20 ° to about 50 °, about 20 ° to about 60 °, about 20 ° to about 70 °. About 20 ° to about 80 °, about 20 ° to about 85 °, about 30 ° to about 40 °, about 30 ° to about 50 °, about 30 ° to about 60 °, about 30 ° to about 70 °, about 30 ° to about 80 °, about 30 ° to about 85 °, about 40 ° to about 50 °, about 40 ° to about 60 °, about 40 ° to about 70 °, about 40 ° to about 80 °, about 40 ° to about 85 °, about 50 ° to about 60 °, about 50 ° to about 70 °, about 50 ° to about 80 °, about 50 ° to about 85 °, about 60 ° to about 70 °, about 60 ° to about 80 °, about 60 ° to about 85 °, about 70 ° to about 80 °, about 70 ° to about 85 °, or about 80 ° to about 85 °. In some cases, the angle formed between the ridge and the bottom surface is about 5 °, about 10 °, about 20 °, about 30 °, about 40 °, about 50 °, about 60 °, about 70 °, about 80 °, or about 85 °. In some cases, the angle formed between the ridge and the bottom surface is at least about 5 °, about 10 °, about 20 °, about 30 °, about 40 °, about 50 °, about 60 °, about 70 °, or about 80 °. In some cases, the angle formed between the ridge and the bottom surface is at most about 10 °, about 20 °, about 30 °, about 40 °, about 50 °, about 60 °, about 70 °, about 80 °, or about 85 °.

In some embodiments, the microneedle portion may also have a bottom surface that is lower than the bottom surface of the support base, e.g., the bottom surface of the microneedle portion may protrude further from the device than the bottom surface of the support base. In some cases, the bottom surface may overlap the base of the microneedle. In some cases, the bottom surface of the inner portion may protrude about 250 μm. In some cases, the bottom surface of the inner portion may protrude about 0 μm, 50 μm, 100 μm, 150 μm, 200 μm, 250 μm, 300 μm, 350 μm, 400 μm, or 450 μm. In some cases, the protruding bottom surface of the inner portion can increase or maintain the surface tension of tissue (such as skin) when the device is applied to the tissue.

In some embodiments, the support substrate 130 may be formed of the same material as the microneedle portion and the microneedles. The support substrate may be thicker than the inner portion. In some cases, the peripheral portion comprises a fiducial marker. Fig. 3 provides a front view of the microneedle device 100 and fiducial markers 310.

In some embodiments, the devices of the present disclosure may be manufactured using a mold. In some cases, the device may be manufactured by moving a resin (e.g., a polyolefin resin) into a mold of the device. The mold may include a plurality of cavities for forming a plurality of microneedles; a cavity for forming an interior portion comprising a plurality of microneedles; and one or more cavities for forming one or more peripheral portions adjacent to the inner portion, wherein the width of the inner portion is less than the width of the peripheral portion. In some cases, the polyolefin resin may generate the plurality of microneedles prior to generating the inner portion. In some cases, the molding resin may be subjected to plasma treatment to modify the surface.

In some embodiments, the devices of the present disclosure may be manufactured using injection molding. In some cases, the device may be manufactured by injecting a heated (e.g., molded) material (e.g., the polyolefin resins described herein) into a mold of the device. The mold may include a plurality of cavities for forming a plurality of microneedles; a cavity for forming an interior portion comprising a plurality of microneedles; and one or more cavities for forming one or more peripheral portions adjacent to the inner portion, wherein a width (e.g., depth and/or thickness) of the inner portion is less than a width of the peripheral portion. In some cases, the heating material may generate a plurality of microneedles prior to generating the interior or microneedle portions.

In some cases, the entire device may be plasma treated, while in other cases, a portion of the device (e.g., a microneedle) is plasma treated. In some cases, a method of plasma treating an apparatus or portion thereof comprises the steps of: providing a device or a portion thereof; placing the device or a portion thereof in a plasma vacuum chamber; closing the plasma vacuum chamber, thereby creating an adequate vacuum seal; evacuating all gases present in the chamber; pumping a plasma processing gas into the plasma vacuum chamber to a desired pressure; enabling the generation of a gas plasma at a desired power for a desired period of time; the chamber of the plasma processing gas is evacuated and the device or portion thereof is removed.

In some cases, the entire device may be excimer laser treated, while in other cases, a portion of the device (e.g., a microneedle) is excimer laser treated. In some cases, a method of excimer laser processing a device or portion thereof comprises the steps of: providing a device or a portion thereof; placing the device or a portion thereof in an excimer laser processing chamber; closing the excimer laser processing chamber; evacuating all gases present in the chamber; pumping a suitable excimer laser processing gas into the excimer laser processing chamber such that the apparatus or a portion thereof substantially absorbs the radiant laser energy; enabling radiation emission of an excimer laser at a desired power for a desired period of time; the excimer laser processing gas is evacuated and the device or part thereof is removed.

In some cases, one or more probes of the plurality of probes are attached to one or more microneedles of the plurality of microneedles. Probes may be attached covalently or non-covalently. In some cases, the probes are attached via linkers or spacers. In some cases, probes are attached to the microneedles in order to achieve maximum packing density on the microneedle surface.

Probe with a probe tip

In some embodiments, the microneedles comprise probes that can bind to and extract biomarkers from tissue. A plurality of probes may be attached to the microneedles of the present disclosure. In some cases, the probe comprises a polynucleotide (e.g., DNA, RNA, cDNA, cRNA, etc.). Polynucleotide probes are often designed to bind or hybridize to specific polynucleotide biomarkers. The present disclosure also provides methods and devices for detecting peptide or protein biomarkers. In these embodiments, probes attached to the microneedles can specifically recognize and bind to a target peptide or target protein. The probe may be any substance capable of binding to a specific peptide or protein biomarker. They may be proteins (e.g., antibodies, antigens, or fragments thereof), carbohydrates, or polynucleotides. Polynucleotides may have sequence specificity for a biomarker, for example, by having complementary sequences.

In some embodiments, the probes to be used are often dependent on the biomarker or biomarkers to be detected. Thus, depending on the nature and number of biomarkers to be detected, the number of probes immobilized to the microneedles may be 2, 3, 4, 5, 6, 7, 8, 9, 10, or more. In some cases, the total number of probes immobilized to the microneedles may be about 1 probe to about 1,000 probes, about 1 probe to about 10,000 probes, about 1 probe to about 100,000 probes, about 1 probe to about 1,000,000 probes, about 1 probe to about 10,000,000 probes, about 1 probe to about 100,000,000 probes, about 1,000 probe to about 100,000,000 probes, about 10,000 probes to about 100,000,000 probes, or about 10,000,000 probes to about 100,000,000 probes. In some cases, the total number of probes in the microneedle is at least about 10, about 20, about 50, about 100, about 500, about 1000, about 10000, about 100000, about 1,000,000, about 10,000,000, or about 100,000,000 probes. In some cases, the total number of probes in the microneedle is less than about 10, about 100, about 1000, about 10000, about 100000, about 1,000,000, about 10,000,000, or about 100,000,000 probes.

In some embodiments, probes for biomarkers may be immobilized onto a plurality of microneedles in a device of the disclosure, particularly for detecting low concentrations of biomarkers, as further described herein. In some cases, a single microneedle comprises a plurality of different probes capable of binding or detecting the same biomarker. In some cases, a single microneedle comprises at least 2 different probes, at least 3 different probes, at least 4 different probes, at least 6 different probes, at least 8 different probes, at least 10 different probes, at least 15 different probes, at least 20 different probes, or at least 50 different probes. In some cases, the same microneedle comprises more than 50 different types of probes for the same biomarker.

In some cases, the same microneedle comprises a plurality of different probes. Different probes may be specific for the same biomarker or for different biomarkers. The microneedle may comprise at least 2 different probes, at least 10 different probes, at least 100 different probes, at least 200 different probes, at least 500 different probes, at least 1,000 different probes, at least 5,000 different probes, or at least 10,000 different probes.

In some cases, individual probes in a plurality of probes are identical (e.g., identical polynucleotides or identical copies of antibodies). In some embodiments, the microneedle can be associated with multiple copies of the same probe (e.g., greater than 2, 5, 10, 50, 100, 1000, 5000, 7500, 10000, or 50000 copies of the same probe). For example, a microneedle may comprise multiple copies of a polynucleotide probe designed to hybridize to the same polymorphism or biomarker. In some cases, the microneedles may comprise multiple copies of antibody probes designed to bind the same epitope.

In some cases, the probe may bind to a class of biomarkers, such as mRNA. Probes that bind to mRNA may comprise polynucleotides having a homopolymer sequence or polynucleotides comprising a homopolymer sequence. In some cases, the probe comprises one or more thymine residues. For example, in some cases, the probe comprises at least 1, at least 5, at least 10, at least 20, at least 25, at least 30, at least 40, at least 50, at least 100, or at least 200 thymine residues (e.g., thymidine residues). In some cases, the probe binds to the poly-a tail of the mRNA molecule. In some cases, the probe comprises a homopolymer sequence. In these cases, the homopolymer sequence is configured to bind to a complementary homopolymer sequence. In some cases, the probe comprises a thymine sequence bound to the poly-a tail of the mRNA. The probe may comprise any fragment of a continuous thymine sequence. In some embodiments, the continuous thymine sequence comprises about 250 continuous thymines. In some embodiments, the continuous thymine sequence comprises about 200 continuous thymines. In some embodiments, the continuous thymine sequence comprises about 150 continuous thymines. In some embodiments, the continuous thymine sequence comprises about 100 continuous thymines. In some embodiments, the continuous thymine sequence comprises about 50 continuous thymines. In some embodiments, the continuous thymine sequence comprises about 50 and 250 continuous thymines. In some cases, the probe may comprise thymine interspersed with other bases.

In some cases, the microneedles comprise polynucleotide probes. Probes can be designed to detect different biomarkers (e.g., nucleic acid molecules, proteins) associated with the same disease, disorder, or condition. In some cases, a first probe may recognize a polymorphism (e.g., DNA polymorphism, RNA polymorphism) associated with a disease, and a second probe may recognize a different polymorphism associated with the same disease. For example, a first nucleic acid probe on a microneedle can be designed to detect a first polymorphism of an RNA biomarker associated with filariasis (a skin condition). The second nucleic acid probe on the microneedle can be designed to detect a second polymorphism of an RNA biomarker associated with filariasis. For example, the polymorphism may be a Single Nucleotide Polymorphism (SNP). Genetic and genomic variations may include a single SNP or multiple SNPs. SNPs may occur at a single locus or at a number of loci. Individuals carrying a particular SNP on an allele at one locus may predictably carry additional SNPs at other loci. The association of SNPs may provide a correlation between alleles that predispose an individual to a disease or condition. In some cases, different polynucleotide probes are designed to detect different biomarkers associated with different conditions. For example, one probe may detect a biomarker (e.g., a polynucleotide) of a disease, while another probe may detect a housekeeping gene or gene product (e.g., a polypeptide). In some cases, the microneedles are attached to polynucleotides, polypeptides, or a mixture of polynucleotides and polypeptides. In some cases, the probe may comprise a negative charge. In some cases, the plurality of probes may comprise a residual negative charge. In some cases, the microneedles of the devices described herein are packed with probes at the maximum packing density of the microneedles. The residual negative charge generated by the largest stacking microneedle may be strong enough to prevent non-specific binding of the stacking probe to a biomarker that is not of interest.

In some cases, the microneedles may also be associated with a plurality of different protein or antibody probes. For example, a first antibody probe on a microneedle can be designed to detect a first epitope of an antigen associated with, for example, skin cancer. The second antibody probe may be designed to detect a second epitope associated with the antigen. Alternatively, in some cases, the second antibody probe may detect epitopes associated with different skin conditions and/or different antigens.

In some cases, the present disclosure provides a microneedle device comprising a set of microneedles, wherein each microneedle in the set comprises the same probe or a set of probes. In some embodiments, the same probe is attached to multiple microneedles of the device. The same probe may be attached to, for example, about 1% of the microneedles, about 5% of the microneedles, about 10% of the microneedles, about 50% of the microneedles, about 90% of the microneedles, about 95% of the microneedles, or about 100% of the microneedles. In some embodiments, the same probe is attached to no greater than 5% of the microneedles, no greater than 10% of the microneedles, no greater than 25% of the microneedles, no greater than 50% of the microneedles, no greater than 95% of the microneedles, or no greater than 99% of the microneedles.

In some cases, the plurality of microneedles may include at least one microneedle attached to a first probe and at least one microneedle attached to a second probe different from the first probe. For example, as described herein, a first probe may be a polynucleotide or polypeptide (e.g., antibody, protein) that specifically binds to a biomarker of a disease or disorder, and a second probe may be a polynucleotide or polypeptide that specifically binds to a different biomarker associated with the same disease or disorder. In some cases, the first probe may be a polynucleotide or polypeptide (e.g., antibody, protein) that specifically binds to a biomarker of a disease or disorder, and the second probe may be a polynucleotide or polypeptide that specifically binds to a different biomarker associated with a different disease, disorder, or condition. In some cases, the different diseases, conditions, or disorders are associated with the same organ. For example, the first probe may be associated with a first disease, disorder, or condition associated with skin; and the second probe may be associated with a second disease, disorder, or condition associated with the skin or eye. In some cases, the device may include an array of microneedles, where each microneedle comprises a probe that detects a biomarker associated with a different disease, disorder, or condition associated with the same organ. The microneedle array may comprise greater than 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 75, 100, 150, 200, 500, or 1000 microneedles associated with different diseases, disorders, or conditions. In some cases, the different diseases, disorders, or conditions are associated with different organs (e.g., greater than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 organs).

In some cases, the microneedle device comprises a plurality of microneedle arrays; the multiple microneedle arrays are often suitable for multiplex reactions. In some cases, the plurality of arrays comprises two or more microneedle arrays, wherein the arrays are designed to detect different biomarkers. In some cases, a first microneedle array can be designed to detect a biomarker associated with a disease, disorder, or condition, and a second microneedle array is designed to detect a different biomarker associated with the same disease, disorder, or condition. In some cases, a first microneedle array can be designed to detect a biomarker associated with a disease, disorder, or condition, and a second microneedle array can be designed to detect a different biomarker associated with a different disease, disorder, or condition. In some cases, the first microneedle array may be designed to detect a plurality of biomarkers associated with a disease, disorder, or condition; and the second microneedle array may be designed to detect multiple biomarkers associated with different diseases, disorders, or conditions. In some cases, the second microneedle array can be designed to detect a control biomarker (e.g., housekeeping gene), whether a positive control or a negative control.

In some embodiments, the probes are covalently attached to the microneedles. In some embodiments, the probes are attached to the microneedles at about 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, or 100% of the maximum bulk density. The maximum bulk density may be determined by the chemical structure of the microneedle surface. In some cases, high density probes may reduce non-specific binding. For example, a high density of polynucleotide probes will prevent non-specific polynucleotide binding due to residual negative charge. In some cases, the microneedles may be treated to facilitate high density probe attachment. For example, the microneedles may be plasma treated. The plasma treatment may include modifying the surface with an oxygen, nitrogen or carbon dioxide plasma.

Microneedle(s)

The microneedles may have a variety of shapes, for example, the microneedles may be spherical, conical, triangular, square, rectangular, pentagonal, hexagonal, heptagonal, octagonal, pyramidal, or any other suitable shape. The microneedles may be sharp microneedles, blunt microneedles, or any combination thereof. For example, a device of the present disclosure comprising a plurality of sharp microneedles may be used to penetrate the skin of a subject, thereby contacting one or more probes on the microneedles with, for example, a biomarker. The sharp microneedles may be used to destroy tissue of a biological sample, such as a cell layer or an outer membrane of cells. The blunt microneedles may be used to contact the skin surface of a subject, thereby contacting the microneedles with, for example, cell surface biomarkers on the skin. In some cases, the present disclosure provides microneedle devices that include microneedles having different shapes (e.g., sharp microneedles and blunt microneedles). In some cases, the microneedles may be solid rather than hollow.

In some cases, the present disclosure provides microneedle devices in which all the needles have the same shape. In some cases, all microneedles on the device have the same shape and size with an error within 5%, 4%, 3%, 2%, 1.5%, 1%, 0.75%, 0.5%, or 0.25%. In some cases, no more than three of the plurality of microneedles do not meet the height tolerance. For example, if the desired microneedle height is 950 μm, the height tolerance may be no less than 600 μm in length and no more than 1050 μm in length.

In some embodiments, the microneedles may have a shape that allows non-specific binding materials to be wiped from the needles as the microneedles leave the skin. In some cases, non-specifically bound material may be wiped away via shear forces generated as the microneedles pass through the skin.

In some embodiments, while the needle-like microneedles may be blunt-tipped objects, they are preferably sharp objects. In some embodiments, the microneedles have a conical structure, wherein the diameter of the base is typically in the range of 10 μm to 500 μm, preferably in the range of 20 μm to 200 μm. Fig. 4 illustrates a surface of a device of the present disclosure having a plurality of microneedles, wherein the diameter width of the base is less than 10mm.

In some embodiments, the microneedles of the present disclosure may have a plurality of different diameters or base widths. For example, the shape of the microneedle base can be, for example, circular, rectangular, triangular, square, pentagonal, hexagonal, heptagonal, or other geometric shape. The microneedles of the present disclosure may have a diameter or base width of no greater than 500 μm, no greater than 400 μm, no greater than 300 μm, no greater than 200 μm, no greater than 100 μm, no greater than 50 μm, no greater than 40 μm, no greater than 30 μm, no greater than 20 μη iota, no greater than 10 μη iota, no greater than 1000nm, no greater than 900nm, no greater than 800nm, no greater than 700nm, no greater than 600nm, no greater than 500nm, no greater than 400nm, no greater than 300nm, no greater than 200nm, or no greater than 100 nm.

In some cases, a microneedle with a conical structure may have a cone edge to cone base angle of 70 ° to about 90 °, 71 ° to 89 °, 72 ° to 88 °, 73 ° to 86 °, 73 ° to 84 °, 73 ° to 80 °, 74 ° to 78 °, 74 ° to 76 °, or 75.5 ° to 76 °. In some cases, a microneedle with a conical structure may have a cone edge to cone base angle of about 73 °, 73.25 °, 73.5 °, 73.75 °, 74 °, 74.25 °, 74.5 °, 74.75 °, 75 °, 75.25 °, 75.5 °, 75.75 °, 76.25 °, 76.5 °, 76.75 °, or 77 °. In some cases, a microneedle with a conical structure can have a cone edge to base angle of less than 73 °, 73.25 °, 73.5 °, 73.75 °, 74 °, 74.25 °, 74.5 °, 74.75 °, 75 °, 75.25 °, 75.5 °, 75.75 °, 76.25 °, 76.5 °, 76.75 °, or 77 °.

In some embodiments, the substrate and the microneedles of the array may be made of a variety of biodegradable or non-biodegradable materials. Examples of materials for the microneedles or the substrate include poly (methyl methacrylate), silicon dioxide, ceramics, metals such as stainless steel, titanium, nickel, molybdenum, chromium, and cobalt, and synthetic or natural resin materials. Some embodiments use biodegradable polymers such as polylactic acid, polyglycolide, polylactic acid-co-polyglycolide, pullulan, caprolactone, polyurethane or polyanhydride. In some other embodiments, non-degradable materials are employed to fabricate the microneedle array, for example, polymeric polycarbonates, synthetic or natural resin materials, such as polymethacrylic acid, ethylene vinyl acetate, polytetrafluoroethylene, polysulfone, or polyoxymethylene. In some embodiments, the microneedles may be insoluble. In some cases, the microneedles are made from thermoplastic polymers.

In some embodiments, the substrate and the microneedle of the array may be made of various thermoplastic polymers. Non-limiting examples of thermoplastic polymers include acrylic polymers such as poly (methyl methacrylate) (PMMA), nylon, polyethylene, polypropylene, polystyrene, polyvinylchloride or teflon. In some cases, the devices of the present disclosure are made from thermoplastic polymers selected from the group consisting of polycarbonate, poly (methyl methacrylate), polyethylene, and polypropylene.

Non-limiting examples of non-degradable polymers include, for example, silicones, hydrogels such as crosslinked poly (vinyl alcohol) and poly (hydroxyethyl methacrylate), ethylene-vinyl acetate, acyl substituted cellulose acetates and alkyl derivatives thereof, partially and fully hydrolyzed alkylene-vinyl acetate copolymers, unplasticized polyvinyl chloride, crosslinked homopolymers and copolymers of polyvinyl acetate, crosslinked polyesters of acrylic and/or methacrylic acid, polyvinyl alkyl ethers, polyvinyl fluoride, polycarbonates, polyurethanes, polyamides, polysulfones, styrene acrylonitrile copolymers, crosslinked polyethylene oxides, polyalkylene, poly (vinyl imidazole), polyesters, poly (ethylene terephthalate), polyphosphazenes, and chlorosulfonated polyolefins, and combinations thereof. In some embodiments, the polymer comprises ethylene vinyl acetate.

Further non-limiting examples of biodegradable polymers include polyesters such as 3-hydroxypropionate, 3-hydroxybutyrate, 3-hydroxyvalerate, 3-hydroxycaproate, 3-hydroxyheptanoate, 3-hydroxyoctanoate, 3-hydroxynonanoate, 3-hydroxydecanoate, 3-hydroxyundecanoate, 3-hydroxydodecanoate, 4-hydroxybutyrate, 5-hydroxyvalerate, polylactide, or polylactic acid, including poly (d-lactic acid), poly (1-lactic acid), poly (d, l-lactic acid), polyglycolic acid and polyglycolide, poly (lactic acid-co-glycolic acid), poly (lactide-co-glycolide), poly (epsilon-caprolactone), and polydioxanone. Polysaccharides including starch, glycogen, cellulose and chitin may also be used as biodegradable materials.

In some embodiments, the substrate and the microneedles of the array may be made of a polyolefin resin. Examples of polyolefin resins include thermoplastic polyolefin: polyethylene (PE), polypropylene (PP), polymethylpentene (PMP) and polybutene-1 (PB-1). Further examples of polyolefin resins include polyolefin elastomers (POE): polyisobutylene (PIB), ethylene Propylene Rubber (EPR), and ethylene propylene diene monomer rubber (class M) (EPDM rubber). In some cases, the polyolefin resin may be a cyclic olefin polymer, such as Zeonor or Zeonex. In some cases, zeonor is Zeonor 1020R, zeonar R or Zeonor 1060R.

In some embodiments, the microneedles employed in the present disclosure have a length (height) typically in the range of 20 μm to 1mmm, preferably in the range of 50 μm to 500 μm. The needle height may be in the range of about 400 μm to about 1000 μm.

The microneedle devices of the present disclosure may include any number of microneedles. In some embodiments, the devices of the present disclosure comprise at least 1 microneedle, at least 100 microneedles, at least 200 microneedles, at least 300 microneedles, at least 400 microneedles, at least 500 microneedles, at least 1000 microneedles, at least 3000 microneedles, or at least 5000 microneedles.

In some embodiments, the devices of the present disclosure include at most 10000 microneedles, at most 5000 microneedles, at most 2500 microneedles, at most 2000 microneedles, at most 1000 microneedles, at most 500 microneedles, at most 400 microneedles, at most 300 microneedles, at most 100 microneedles, at most 90 microneedles, at most 50 microneedles, at most 40 microneedles, at most 30 microneedles, at most 20 microneedles, at most 15 microneedles, at most 10 microneedles, at most 9 microneedles, at most 8 microneedles, at most 7 microneedles, at most 6 microneedles, at most 5 microneedles, at most 4 microneedles, at most 3 microneedles, at most 2 microneedles, or 1 microneedle.

In some embodiments, the devices of the present disclosure comprise from about 1 microneedle to about 100 microneedles, from about 1 microneedle to about 200 microneedles, from about 1 microneedle to about 1000 microneedles, from about 10 microneedles to about 200 microneedles, from about 50 microneedles to about 150 microneedles, or from about 1 microneedle to about 5000 microneedles.

In some embodiments, the device of the present disclosure comprises 100 microneedles. In some embodiments, the devices of the present disclosure comprise 90 to 110 microneedles. In some embodiments, the devices of the present disclosure comprise 80 to 120 microneedles. In some embodiments, the devices of the present disclosure comprise 50 to 150 microneedles.

In some embodiments, for devices containing multiple microneedles, the microneedles may be present on the device in rows. In some embodiments, the rows may be separated by a space that is nearly equal to the space of the aligned needles in the rows. In some embodiments, the rows may be separated by irregular spacing.

In some embodiments, the density of the microneedles in the device may be at least about 10/cm ² About 15/cm ² About 20/cm ² About 25/cm ² About 30/cm ² About 35/cm ² About 40/cm ² About 45/cm ² About 50/cm ² About 55/cm ² About 60/cm ² About 65/cm ² About 70/cm ² About 75/cm ² About 80/cm ² About 85/cm ² About 90/cm ² About 95/cm ² About 100/cm ² About 110/cm ² About 120/cm ² About 130/cm ² About 140/cm ² About 150/cm ² Or about 200/cm ² . In some embodiments, the density of microneedles in the device may be less than about 10/cm ² About 15/cm ² About 20/cm ² About 25/cm ² About 30/cm ² About 35/cm ² About 40/cm ² About 45/cm ² About 50/cm ² About 55/cm ² About 60/cm ² About 65/cm ² About 70/cm ² About 75/cm ² About 80/cm ² About 85/cm ² About 90/cm ² About 95/cm ² About 100/cm ² About 110/cm ² About 120/cm ² About 130/cm ² About 140/cm ² About 150/cm ² Or about 200/cm ² 。

In some embodiments, the distance between the centers of two microneedles on a device of the present disclosure can be calculated to determine the density of the microneedles in the device. In some embodiments, the center-to-center distance between two microneedles may be less than 1000 μm, less than 900 μm, less than 800 μm, less than 700 μm, less than 600 μm, less than 500 μm, less than 400 μm, less than 300 μm, less than 200 μm, or less than 100 μm. In some embodiments, the center-to-center distance between two microneedles may be no greater than 100 μm, no greater than 200 μm, no greater than 300 μm, no greater than 400 μm, no greater than 500 μm, no greater than 600 μm, no greater than 700 μm, no greater than 800 μm, no greater than 900 μm, or no greater than 1000 μm.

In some embodiments, the microneedles of the present disclosure may have a plurality of different diameters or base widths. For example, the shape of the microneedle base can be, for example, circular, rectangular, triangular, square, pentagonal, hexagonal, heptagonal, or other geometric shape. The microneedles of the present disclosure may have a diameter or base width of no greater than 500 μm, no greater than 400 μm, no greater than 300 μm, no greater than 200 μm, no greater than 100 μm, no greater than 50 μm, no greater than 40 μm, no greater than 30 μm, no greater than 20 μm, no greater than 10 μm, no greater than 1000nm, no greater than 900nm, no greater than 800nm, no greater than 700nm, no greater than 600nm, no greater than 500nm, no greater than 400nm, no greater than 300nm, no greater than 200nm, or no greater than 100 nm.

Kit for detecting a substance in a sample

In some embodiments, the present disclosure provides a kit comprising a microneedle device as described herein. In some cases, a kit may include a microneedle device as described herein, wherein the microneedle device includes a recess behind a microneedle zone and an applicator that fits within the recess. In some cases, the applicator is a mechanical applicator. In some cases, the microneedle device comprises: a microneedle zone comprising a plurality of microneedles protruding from a front surface of a microneedle base substrate, the microneedle base substrate further comprising a rear surface comprising a recess directly behind at least a portion of the microneedle zone; and a support substrate adjacent to at least one side of the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate.

Method

In some embodiments, the present disclosure provides a method of obtaining a biological sample from tissue, such as skin of a subject. The subject may have any age. For example, the subject may be an elderly person, an adult, a adolescent, a pre-pubertal adolescent, a child, a young child, or an infant. The subject may be a mammal, bird, fish, reptile or amphibian. Non-limiting examples of subjects include humans, primates, dogs, cats, horses, pigs, and mice. The object may be a patient. In some embodiments, the subject is a human.

In some cases, the method includes contacting a device of the present disclosure with tissue of a subject. In some cases, the subject may bring the device into contact with its tissue, for example, by applying the device to its skin. In some cases, the device may be applied to the tissue of the subject by other people. In some cases, the tissue may be skin, and the skin may be cleaned prior to application of the device.

In some cases, the method includes contacting a device of the present disclosure with tissue, such as skin, of a subject. In some cases, the tissue is in situ tissue. In some cases, the tissue is a biopsy. In some cases, the tissue is skin tissue. Pressure may be applied to the device, causing the microneedles to penetrate the tissue. In some cases, pressure is applied by hand, such as thumb pressure. In other cases, pressure is applied by an applicator. The pressure may be less than about 1N/mm2, 5N/mm2, 10N/mm2, 50N/mm2, 100N/mm2, 200N/mm2, or in the range of about 1N/mm2 and about 200N/mm 2. The applicator may be a manual applicator or a mechanical or electronic applicator that provides uniform pressure. In some cases, the width of one end of the applicator is less than the width of the groove behind the microneedle portion. The device surface distal to the microneedle may be referred to as the back surface. The pressure may be applied to the entire back surface or may be concentrated on the back surface of the interior portion of the device 120.

In some embodiments, the microneedle device can remain in the tissue for up to 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, 10 minutes, 12 minutes, 14 minutes, 16 minutes, 18 minutes, 20 minutes, 22 minutes, 24 minutes, 26 minutes, 28 minutes, or 30 minutes. The residence time of the microneedles within the tissue allows the probes to contact and bind to biomarkers (e.g., nucleic acid molecules) in the tissue. In some cases, the residence time of the microneedles within the tissue may be optimized for a particular tissue, biomarker, or probe. For example, a biomarker, such as an RNA biomarker, may begin to degrade after a given period of time, and thus a residence time long enough to allow binding, but short enough to minimize degradation, would be preferred. In some cases, a residence time of about 1 minute to about 10 minutes may be preferred for RNA biomarkers. In some cases, a residence time of less than 5 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 5 minutes may be preferred for RNA biomarkers. In some cases, a residence time of no more than 10 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 2 minutes to about 9 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 3 minutes to about 8 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 4 minutes to about 7 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 5 minutes to about 6 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 5 minutes to about 7 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 5 minutes to about 8 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 5 minutes to about 9 minutes may be preferred for RNA biomarkers. In some cases, a residence time of about 5 minutes to about 10 minutes may be preferred for RNA biomarkers.

In some cases, the optimal residence time of the microneedle within the tissue is determined by the cycle threshold (Ct) for detecting the target biomarker. In some cases, for RNA biomarkers, ct is lowest at about 5 minutes. In some cases, for RNA biomarkers, the Ct of the collected sample after a residence time of 10 minutes is higher than the Ct of the collected sample after a residence time of 5 minutes. In some cases, an increase in Ct from a minimum Ct indicates that degradation of the sample may be occurring.

In some cases, the optimal residence time results in a high quality biomarker sample. In some cases, for an RNA biomarker, the quality of the RNA sample is determined by the ratio of the larger RNA fragment (e.g., an RNA fragment comprising about 700 bases or more) contained in the RNA sample to the smaller RNA fragment (e.g., a fragment comprising about 150 bases to about 200 bases) contained in the RNA sample. In some cases, a residence time of no more than 10 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of no more than 9 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of no more than 8 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of no more than 7 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of no more than 6 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of no more than 5 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of no more than 4 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of no more than 3 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1. In some cases, a residence time of about 5 minutes generates an RNA sample, wherein the RNA sample comprises a population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) and a population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases), and wherein the ratio of the population of large RNA fragments (e.g., RNA fragments comprising about 700 bases or more) to the population of small RNA fragments (e.g., fragments comprising about 150 bases to about 200 bases) is greater than 1.

In some embodiments, the device is removed from the tissue after a time that the probe is allowed to contact and bind to the biomarker. Removal of the microneedles from the tissue may generate shear pressure to remove non-specifically bound contaminants from the microneedles. In some cases, the contaminant includes any compound that is not intended for further genomic analysis.

In some embodiments, once removed from the skin, the device and extracted RNA biomarker may be placed in a storage buffer, a transport buffer, or an assay buffer. The device and extracted RNA biomarker can be stored at-80 ℃, -20 ℃, -4 ℃, 4 ℃ or room temperature. Alternatively, the device may be placed in a buffer to dissociate the extracted RNA biomarker from the device, and the extracted RNA biomarker may be stored at-80 ℃, -20 ℃, -4 ℃, or room temperature. The extracted RNA biomarker (with or without the device) may be sent to a laboratory for further analysis.

In some embodiments, the present disclosure provides a method for preparing a sample for genomic or transcriptomic analysis. The method can include contacting a device of the present disclosure with skin of a subject, extracting nucleic acid biomarkers from the skin onto the device, washing the nucleic acid biomarkers from the device to produce a sample solution. The nucleic acid of the sample solution may be analyzed by polymerase chain reaction. In some cases, the extracted nucleic acids of the sample solution may be amplified and analyzed by Next Generation Sequencing (NGS). NGS extracting mRNA can determine gene expression in the subject's contact skin.

In some embodiments, the present disclosure provides a method of determining whether a skin lesion on a subject having a skin lesion will respond to an autoimmune therapeutic drug. For example, the present disclosure provides a method of preparing a biological sample and identifying one or more autoimmune disease treatment drugs for a subject, the method comprising contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a solid microneedle; and applying pressure to the microneedle device such that the microneedle device penetrates the skin of the subject. An extracted ribonucleic acid (RNA) molecule from a subject may be obtained by removing the microneedle device from the skin of the subject. High throughput sequencing of the extracted RNA molecules can generate one or more sequence reads of the subject, which can be aligned with a known sequence read signature, wherein the known sequence read signature is associated with a positive response to an autoimmune disease therapeutic drug, thereby obtaining aligned sequence reads; and for classifying the subject as having a likelihood of having greater than 50%, 60%, 70%, 80% or 90% of responding to the autoimmune disease treatment drug by applying a trained algorithm to the aligned sequence reads. In some cases, the trained algorithm has a positive predictive value greater than 50%, 60%, 70%, 80%, or 90% or a negative predictive value greater than 50%, 60%, 70%, 80%, or 90%. The autoimmune disease treatment drug may be an IL-17 mediated therapy, an IL-23 mediated therapy, a TNFa mediated therapy, or a combination thereof. The method may comprise the steps of: mRNA is extracted and sequenced as described above, and the genetic signature of the lesion is compared to known genetic expression signatures using a trained algorithm, or analyzed using a trained algorithm. In some cases, the trained algorithm may have a positive predictive value of greater than 50% or a negative predictive value of greater than 50% for predicting a likelihood of a positive response to an autoimmune disease therapeutic drug. Comparing the gene expression signatures may allow classifying the contact skin lesions as likely to be responsive to a treatment, wherein the treatment is an IL-17 mediated treatment, an IL-23 mediated treatment, a tnfa mediated treatment, or a combination thereof. Examples of autoimmune disease therapeutic agents include etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan, buddamab, antique mab, ti Qu Jizhu mab, and Li Sanji mab.

In some embodiments, the present disclosure provides a method for determining the likelihood that a subject suffering from an autoimmune disease will respond to a therapeutic agent for an autoimmune disease, the method comprising obtaining a biological sample from the subject; extracting one or more nucleic acid molecules from the biological sample; high throughput sequencing the nucleic acid molecules to generate one or more sequence reads of the subject; and performing genomic analysis on the sequence reads. Genomic analysis may include: determining the level of gene expression encoded by the sequence reads; comparing the expression level to a known positive response genetic signature characteristic of treatment of an autoimmune disease; and generating a treatment score from the comparison, wherein the treatment score is specific to the patient's response to the treatment.

In some embodiments, the present disclosure provides a method of treating a skin lesion in a subject, the method comprising contacting the skin lesion with a device as described herein; extracting mRNA from skin lesions; determining a gene expression signature using NGS; comparing the signature to known signatures or using a trained algorithm to select a treatment for the subject; and administering the selected treatment to the subject. In some cases, selecting the therapy includes comparing the expression level to a known positive response genetic signature characteristic of the therapy for the autoimmune disease.

The present disclosure provides non-invasive and/or minimally invasive devices, methods, and systems that provide a subject-specific data set for disease diagnosis or guiding clinical intervention. Microneedle devices and methods and systems for their use are also described herein. For example, in some embodiments, the methods of the invention described herein may include determining a gene expression profile, an accurate classification of a disease or condition, a prospective guided therapy and/or clinical intervention of a disease or condition, retrospective analysis and modeling of past treatments, identifying an activation pathway of a disease or condition, or any combination thereof, for one or more subjects. The microneedle devices described herein can provide accurate minimally invasive and/or non-invasive sample collection that can be used for downstream analysis as described elsewhere herein, particularly in the foregoing methods. Thus, the methods of the present disclosure can be used to provide methods for practicing personalized medicine, wherein the treatment is tailored to a subject based on the disease characteristics of the subject (e.g., the gene expression profile of the subject suffering from the disease).

In general, microneedle devices can facilitate in situ collection of a biological sample of a subject for further analysis. In some cases, the microneedle device may include one or more microneedle features configured to pierce the skin or mucosa of a subject. In some cases, the mucosa may include oral mucosa, ocular mucosa, or any combination thereof. When there are multiple microneedles, the device may include a planar base that supports the microneedles. The substrate may be made of the same material as the microneedles. It can also be made of different materials. In some cases, the microneedles may further include probes attached to the microneedles. These probes may be configured to bind to one or more biomarkers within a sample or tissue (e.g., skin) of a subject in order to allow for extraction of the biomarkers for further analysis. The extracted biomarkers can be analyzed (e.g., to generate a genetic signature or gene expression profile) alone or in combination to provide a diagnosis or prediction of the subject's response to a drug or other treatment. Treating a mild, moderate or severe form of skin condition in a subject

In some cases, known treatments of known diseases may be ineffective against a particular form of disease, as compared to alternative forms of the same disease. Differences between disease forms may arise due to differences in the genetic signature of each particular form. In some embodiments, the methods described herein can provide targeted, specialized, personalized, or other highly effective treatments or treatment recommendations for a subject suffering from a particular form of a known disease.

In some embodiments, the present disclosure provides a method 600 of determining whether a subject with a mild, moderate, or severe form of a skin condition will respond to a recommended treatment, as seen in fig. 6. In some cases, a method of determining whether a skin condition of a subject having the skin condition will respond to a recommended treatment may include: (a) Obtaining ribonucleic acid (RNA) biomarker 602 from a subject (as described elsewhere herein); (b) Determining a likelihood that the skin condition will respond to a recommended treatment, wherein the recommended treatment has a total effective rate 604 of 50% or less for a known form of skin condition; and (c) treating the subject 606 with the recommended treatment.

In some cases, the determining step (i.e., step (b) above) may further include: (i) High throughput sequencing of RNA biomarkers to generate one or more sequence reads of the subject; (ii) Aligning the one or more sequence reads of the subject with a known sequence read signature, wherein the known sequence read signature is associated with a positive response to the recommended treatment, thereby obtaining aligned sequence reads; and (iii) classifying the subject as having a likelihood of producing a positive response to the recommended treatment by applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 50% for a likelihood of predicting a positive response to the recommended treatment of greater than 50%. In some cases, the RNA biomarker may comprise an RNA molecule. In some cases, the RNA biomarker is transcribed from one or more of the following genes listed in table 6, table 12, or table 13.

In some embodiments, the trained algorithm may also have a negative predictive value of greater than 50% for predicting a likelihood of greater than 50% of positive responses to the recommended treatment. In some cases, the positive predictive value may be greater than 50%, 60%, 70%, 80%, or 90%. In some cases, the negative predictive value may be or greater than 50%, 60%, 70%, 80%, or 90%.

In some embodiments, the recommended treatment may include one or more autoimmune therapeutic drugs against an autoimmune disease or condition. In some cases, the autoimmune disease or condition may be psoriasis. In some cases, the recommended treatment may include etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan antibody, buddamab, antique mab, ti Qu Jizhu mab, li Sanji bead mab, or any combination thereof.

Recommending treatment by positive predictive value

In some cases, one or more subjects with similar diseases or conditions may respond to treatment with varying efficacy. In some embodiments, the efficacy may include responders, non-responders, or adverse responders. In particular, genetic variability between subjects suffering from similar diseases or conditions can also stratify the different efficacy of treatments aimed at broadly treating general forms of diseases or conditions. In some embodiments, aspects of the disclosure provided herein include a method 608 of preparing a biological sample and identifying one or more therapeutic drugs to effectively tailor a treatment to a particular genetic variant of a disease or condition of a subject, as seen in fig. 6B.

In some embodiments, the present disclosure provides a method of treating a subject suffering from an autoimmune skin disorder (e.g., psoriasis) or recommending a treatment for such a subject, comprising collecting from the subject a sample comprising skin-derived RNA. Such collection of RNA samples may involve the use of a microneedle device or may involve lysing tissue biopsies or cell samples and extracting nucleic acids (e.g., RNA) using an RNA extraction protocol. In some cases, the subject has not been administered an autoimmune therapeutic drug prior to collecting the sample. For example, in some embodiments, the subject has not been administered an autoimmune therapeutic drug for more than 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 10 days, 15 days, or 20 days prior to collecting a sample from the subject. In some cases, the method further comprises determining the expression level of at least one gene based on the RNA; predicting that a subject suffering from the autoimmune skin disorder will respond to the autoimmune therapeutic agent with greater than 70% positive predictive value, greater than 75% positive predictive value, greater than 80% positive predictive value, greater than 85% positive predictive value, greater than 90% positive predictive value, or greater than 95% positive predictive value based on the expression level of the at least one gene; and/or treating the subject with the autoimmune therapeutic drug based on the prediction. In some cases, the positive predictive value is determined using a queue of a particular size. For example, the method can have a positive predictive value provided herein when evaluated in a cohort of greater than 5, 10, 25, 50, 100, 150, 200, 250, 500, or 1000 patients.

In some embodiments, the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an autoimmune therapeutic drug, comprising collecting a sample comprising skin-derived RNA from the subject. Such collection of RNA samples may involve the use of a microneedle device or may involve lysing tissue biopsies or cell samples and extracting nucleic acids (e.g., RNA) using an RNA extraction protocol. In some cases, the subject has not been administered an autoimmune therapeutic drug prior to collecting the sample. For example, in some embodiments, the subject has not been administered an autoimmune therapeutic drug for more than 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 10 days, 15 days, or 20 days prior to collecting a sample from the subject. In some cases, the method further comprises converting the RNA to cDNA and determining the expression level of at least one gene based on the cDNA. In some cases, the method further comprises predicting that a subject with the skin lesion will respond to the autoimmune therapeutic drug with a positive predictive value of greater than 70%, a positive predictive value of greater than 75%, a positive predictive value of greater than 80%, a positive predictive value of greater than 85%, a positive predictive value of greater than 90%, or a positive predictive value of greater than 95% based on the expression level of the at least one gene; and/or treating the subject with the autoimmune therapeutic drug based on the prediction. In some cases, the positive predictive value is determined using a queue of a particular size. For example, the method can have a positive predictive value provided herein when evaluated in a cohort of greater than 5, 10, 25, 50, 100, 150, 200, 250, 500, or 1000 patients.

In some embodiments, the present disclosure provides a method of determining whether a skin lesion of a subject will respond to an autoimmune therapeutic drug comprising penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a microneedle. The RNA molecules are obtained from the subject by removing the microneedle device from the skin of the subject. In some cases, RNA molecules are subjected to high throughput sequencing to generate sequence reads. Sequence reads are aligned with sequence read signatures associated with positive responses to autoimmune disease therapeutic drugs to obtain aligned sequence reads. Applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 70%, 80%, 85%, 90% or 95% for predicting a response to the autoimmune disease therapeutic drug. In some cases, the expression level of at least one RNA molecule is determined using aligned sequence reads; and applying a trained algorithm to the expression level of the at least one RNA molecule, wherein the trained algorithm predicts whether the subject with a skin disorder will respond to IL-17 mediated therapy, IL-23 mediated therapy, tnfa mediated therapy, or any combination thereof. In some cases, high throughput sequencing of RNA biomarkers is performed to generate one or more sequence reads of the subject. The one or more sequence reads are aligned with a known sequence read signature, wherein the known sequence read signature is associated with a positive response to the recommended treatment, thereby obtaining aligned sequence reads. Classifying a subject as having a likelihood of producing a positive response to a recommended treatment by applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 50%, 60%, 70%, 80%, 85%, 90%, or 95% for predicting a positive response to the recommended treatment. In some cases, the trained algorithm also has a negative predictive value greater than 50%, 60%, 70%, 80%, 85%, 90%, or 95%. In some cases, the recommended treatment includes one or more autoimmune therapeutic drugs for an autoimmune disease or condition, including, for example, etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan antibody, buddamab, coumarone mab, tem Qu Jizhu mab, and Li Sanji beadmab. In some cases, the autoimmune disease or condition is psoriasis, acne, atopic dermatitis (i.e., eczema), raynaud's phenomenon (Raynaud's phenomenon), rosacea, skin cancer (e.g., basal cell carcinoma, squamous cell carcinoma, and melanoma), or vitiligo.

In some embodiments, the present disclosure provides a method of determining whether a subject suffering from an autoimmune skin disorder will respond to an autoimmune therapeutic drug, comprising: extracting mRNA from the skin of the subject; sequencing the mRNA from the skin of the subject; and predicting whether the subject having the autoimmune disorder will respond to etanercept, adalimumab, infliximab, cetuximab, secukinumab, iximab Bei Shan, buddamab, gulkumab, ti Qu Jizhu mab, and Li Sanji bead mab with a positive predictive value of greater than 50%, 60%, 70%, 80%, 85%, 90%, or 95%.

In some cases, the at least one gene is at least one gene from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least two genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least three genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least four genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least five genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least six genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least ten genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least fifteen genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least twenty genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least 25 genes from table 6, table 12, and/or table 13. In some cases, the at least one gene is at least 50 genes from table 6, table 12, and/or table 13.

In some cases, one or more genes used to detect a response to a treatment may be involved in a common pathway, such as an IL-17 mediated pathway, a TNF- α pathway, or an IL-23 pathway. In some cases, the at least one gene comprises at least 2, 3, 4, 5, 6, 7, 10, 15, or 20 genes that do not share a common upstream regulator or do not share a common pathway. In some cases, the gene has a relationship with the recommended treatment, e.g., the gene may be involved in a particular pathway (e.g., IL-17, IL-23, or TNF- α mediated pathway). In some cases, at least one gene has no known relationship to the recommended treatment. For example, a gene used to evaluate a response to treatment with a particular therapeutic agent may not be involved in the pathway for which the therapeutic agent is directed. In some cases, the autoimmune therapeutic is an IL-17 mediated therapy, and the at least one gene includes at least one gene that is not involved in an IL-17 mediated pathway. In some cases, the autoimmune therapeutic is an IL-23 mediated therapy, and the at least one gene includes at least one gene that is not involved in an IL-23 mediated pathway. In some cases, the autoimmune therapeutic is a TNF- α mediated therapy and the at least one gene includes at least one gene that is not involved in a TNF- α mediated pathway.

In some cases, a trained algorithm is applied to the expression level of at least one gene to predict the likelihood that a subject will respond to a therapeutic agent. In some cases, the algorithm is trained using samples from patients who were administered a single type of drug. For example, the patient may be administered IL-17 mediated therapy, TNF- α mediated therapy, or IL-23 mediated therapy.

In some cases, the method includes using a microneedle device. In some cases, a method of preparing a biological sample and identifying one or more therapeutic agents may comprise: (a) Contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a microneedle, as described elsewhere herein 610; (b) Applying pressure to the microneedle device such that the microneedle device penetrates the skin 612 of the subject; (c) Obtaining extracted RNA molecules 614 from the subject by removing the microneedle device from the skin of the subject; (d) High throughput sequencing the extracted RNA molecules to generate one or more sequence reads 616 of the subject; (e) Aligning the one or more sequence reads of the subject with a known sequence read signature, wherein the known sequence read signature is associated with a positive response to the one or more therapeutic agents, thereby obtaining an aligned sequence read 618; and classifying the subject as having a likelihood of producing a positive response to the one or more therapeutic agents by applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 70%, a positive predictive value of greater than 75%, a positive predictive value of greater than 80%, a positive predictive value of greater than 85%, a positive predictive value of greater than 90%, or a positive predictive value of greater than 95%. In some embodiments, the method of preparing a biological sample and identifying one or more therapeutic agents may further comprise administering the one or more therapeutic agents to the subject. In some cases, the autoimmune disease or condition is psoriasis.

In some cases, the subject has a PASI of at least 8, at least 9, at least 10, at least 15. In some cases, the subject has a PASI of at least 8 and a PASI of at least 75, 80, 90, 95, or 100 after treatment with the recommended medication. In some cases, the subject has a PASI of at least 10 and a PASI of 75, 80, 90, 95, or 100 after treatment. In some embodiments, the trained algorithm may also have a negative predictive value of greater than 50% for predicting a likelihood of greater than 50% of positive responses to one or more therapeutic agents. In some cases, the positive predictive value may be greater than 50%, 60%, 70%, 80%, or 90%. In some cases, the negative predictive value may be or greater than 50%, 60%, 70%, 80%, or 90%. In some embodiments, the extracted RNA molecule may be transcribed from one or more of the following genes listed in table 6, table 12, or table 13. In some embodiments, the recommended treatment may include one or more autoimmune therapeutic drugs against an autoimmune disease or condition. In some cases, the autoimmune disease or condition may be psoriasis. In some cases, the recommended treatment may include etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan antibody, buddamab, antique mab, ti Qu Jizhu mab, li Sanji bead mab, or any combination thereof.

Response to treatment

In some cases, variability in gene expression between one or more subjects may predict a response to a particular treatment of a disease or condition, particularly psoriasis. In some embodiments, aspects of the invention disclosed herein provide a method for determining a response of a subject suffering from a disease or condition to treatment for the disease or condition. In some embodiments, the method may further comprise recommending additional treatments specific to the score. In some cases, the method may further comprise administering the therapy or the additional therapy to the subject. In some cases, the one or more nucleic acid molecules may include a deoxyribonucleic acid (DNA) molecule. In some cases, the one or more nucleic acid molecules comprise RNA molecules. In some cases, the RNA molecule may include a messenger RNA (mRNA) molecule.

In some embodiments, the disease or condition may be an autoimmune disease. In some cases, the autoimmune disease may be psoriasis.

Determining a mild, moderate or severe form of a known disease or condition

In some cases, a particular form of disease (e.g., severe, moderate, normal, etc.) may contain different genetic signatures. Certain known treatments for known diseases or conditions are not effective against a particular form of disease, as compared to alternative forms of the same disease that may be included. In some cases, the method may further include recommending a treatment specific to the score. In some cases, the method may further comprise administering the treatment to the subject. In some cases, the disease or condition may be an autoimmune disease. In some cases, the autoimmune disease may be psoriasis. In some cases, the one or more nucleic acid molecules may include a DNA molecule. In some embodiments, the one or more nucleic acid molecules may include an RNA molecule. In some cases, the RNA molecule may comprise an mRNA molecule.

In some embodiments, the at least five genes from table 6, table 12, or table 13 may include genes associated with one or more pathways. In some cases, the at least five genes from table 6, table 12, or table 13 may include at least 2 genes associated with activation of one or more pathways.

Identification of activation of biological pathways associated with a disease or condition

In some embodiments, aspects of the present disclosure provide a method 642 for identifying activation of one or more pathways that may be associated with a disease or condition of a subject, as shown in fig. 6C. In some embodiments, a method for identifying activation of one or more pathways that may be associated with a disease or condition of a subject may comprise the steps of: (a) Contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes 644 coupled to a microneedle; (b) Applying pressure to the microneedle device such that the microneedle device penetrates the skin 646 of the subject; (c) Obtaining extracted RNA molecules 648 from the subject by removing the microneedle device from the skin of the subject; (d) High throughput sequencing the extracted RNA molecules to generate one or more sequence reads 650 of the subject; (e) Aligning the one or more sequence reads of the subject with a known sequence read signature, wherein the known sequence read signature may be associated with activation of one or more pathways associated with a disease or condition 652; and (f) classifying the subject as having a likelihood of developing greater than 50% of the disease or condition associated with activation of the one or more pathways by applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 50% or a negative predictive value of greater than 50% for a likelihood of predicting developing greater than 50% of the disease associated with activation of the one or more pathways 654. In some cases, the method may further comprise recommending a treatment specific to activation of one or more pathways. In some cases, the method may further comprise administering to the subject a treatment specific for activation of one or more pathways. In some cases, the microneedles may be solid microneedles.

In some embodiments, the trained algorithm may also have a negative predictive value of greater than 50% for predicting a likelihood of developing greater than 50% of the disease or condition associated with activation of the one or more pathways. In some cases, the positive predictive value may be greater than 50%, 60%, 70%, 80%, or 90%. In some cases, the negative predictive value may be or greater than 50%, 60%, 70%, 80%, or 90%.

In some embodiments, the disease or condition may be an autoimmune disease or condition. In some cases, the autoimmune disease or condition may be psoriasis. In some embodiments, the extracted RNA molecules may be transcribed from one or more genes, including at least one gene associated with activation of one or more pathways. In some cases, the one or more genes may include at least two genes associated with activation of one or more pathways. In some cases, the one or more genes may include any of the genes listed in table 6, table 12, or table 13.

Object(s)

As used herein, the term "subject" refers to any animal, including mammals and non-mammals, such as humans, non-human primates (e.g., rhesus, cynomolgus, residual tail, pigtail, squirrel, cat owl, baboon, chimpanzee, marmoset, spider monkey, etc.), rodents (e.g., mice, rats, guinea pigs, etc.), dogs, cats, pigs, etc. In some cases, the object is a human object.

In some embodiments, the subject may experience or have symptoms of a skin condition. Examples of skin conditions that may be detected and/or treated by the methods and devices provided herein include: psoriasis, acne, atopic dermatitis (i.e., eczema), raynaud's phenomenon, rosacea, skin cancers (e.g., basal cell carcinoma, squamous cell carcinoma, and melanoma), and vitiligo. In some cases, the subject has one or more skin conditions (e.g., psoriasis and atopic dermatitis) or has symptoms of the skin condition. In some cases, the subject may experience varying degrees of skin pathology, e.g., mild, moderate, severe, and very severe. Examples of psoriasis symptoms include red skin patches covered with thick silver scales; small scale spots; dry skin, chapped skin, possible bleeding or itching; itching, burning or soreness; nail thickening, recessing or ridging; swelling and stiffness of the joints.

Psoriasis Area and Severity Index (PASI) score

Psoriasis Area and Severity Index (PASI) scores are commonly used to measure the discoloration, thickness, scaling and coverage of these plaques. A care giver (e.g., doctor, nurse, medical nurse, physician's assistant, etc.) may use the PASI score to measure the severity and extent of psoriasis and observe the effectiveness of the psoriasis treatment. The use of the tool also allows the caregiver to monitor the progress of the condition and evaluate the effectiveness of the treatment.

The scoring involves ranking the symptoms of psoriasis from none to very severe and estimating the percentage of the body they affect. The PASI score was also used by researchers to determine the effectiveness of psoriasis drugs in clinical trials.

The absolute PASI score ranges from 0 to 72, with higher scores indicating a higher severity of psoriasis. A score of 0 indicates no psoriasis, while a score higher than 10 indicates severe psoriasis. The scoring system includes one intensity component and another body coverage component. Fractional intensity part measures 1) discoloration (i.e., redness), 2) thickness, and 3) scale. The area fraction shows the extent of the effect of psoriasis on: 1) head and neck, 2) upper limbs, 3) torso and 4) lower limbs.

PASI score the PASI score may be calculated as follows:

clinical researchers often use PASI percent response rates to indicate treatment outcome. For example, PASI 75 means that one's PASI score is reduced by 75% from baseline, which indicates a significant improvement in pathology. Conventional treatment targets are to achieve PASI 75. However, PASI 90 or PASI 100 (i.e., a 90% or 100% decrease in PASI score, respectively) is more desirable.

In some cases, the subject provided herein has a PASI of at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or at least 15. In some cases, the subject has moderate to severe psoriasis and has a baseline PASI of greater than or equal to 10. In some cases, the methods provided herein provide a prediction of whether a subject will respond to a particular treatment. In some cases, after treatment by recommended treatment, the subject responds to the treatment and the PASI score decreases. In some cases, the subject's response to treatment may be a specific response at week 16 after treatment, such as week 16 PASI 60, week 16 PASI 75, week 16 PASI 80, week 16 PASI 90, week 16 PASI 95, week 16 PASI 99, or week 16 PASI 100. In some cases, the subject's response to treatment may be a specific response at week 12 after treatment, such as week 12 PASI 60, week 12 PASI 75, week 12 PASI 80, week 12 PASI 90, week 12 PASI 95, week 12 PASI 99, or week 12 PASI 100. In some cases, the subject's response to treatment is a response at week 8 after treatment, such as week 8 PASI 60, week 8 PASI 75, week 8 PASI 80, week 8 PASI 90, week 8 PASI 95, week 8 PASI 99, or week 8 PASI 100. In some cases, the subject's response to treatment is a response at week 4 after treatment, such as week 4 PASI 60, week 4 PASI 75, week 4 PASI 80, week 4 PASI 90, week 4 PASI 95, week 4 PASI 99, or week 4 PASI 100. In some embodiments, the subject's response to treatment is assessed at least 2 weeks, at least 3 weeks, at least 4 weeks, at least 8 weeks, at least 12 weeks, at least 16 weeks, at least 20 weeks, or at least 24 weeks after treatment. In some embodiments, the subject's response to treatment is assessed at most 2 weeks, at most 3 weeks, at most 4 weeks, at most 8 weeks, at most 12 weeks, at most 16 weeks, at most 20 weeks, at most 24 weeks, or at most 50 weeks after treatment. For example, in some embodiments, the subject's response to treatment may be evaluated between 8 weeks and 12 weeks after treatment, between 4 weeks and 12 weeks after treatment, and/or between 8 weeks and 16 weeks after treatment. In some embodiments, at any time period after treatment, the patient's PASI response may be at least PASI 60, at least PASI 70, at least PASI 80, at least PASI 90, at least PASI 95, at least PASI 99, or PASI 100.

Sample preparation

Nucleic acids may be extracted from a sample using a microneedle device as described herein, or directly from a sample (e.g., a biopsy sample) using an extraction method other than that involving the use of a microneedle device. Types of nucleic acids that can be extracted include any nucleic acid molecule encoding genetic information, including, for example, messenger RNA (mRNA), microrna, DNA (e.g., nuclear DNA or mitochondrial DNA), mixtures of mRNA and DNA, short RNAs, isolated RNAs, and isolated DNA. Biopsy samples that may be used for nucleic acid extraction may include any tissue taken from any part of the body, such as from a body surface (e.g., skin, hair, scalp, surface skin, nails, skin fragments, hair follicles); is obtained from the body (e.g., brain, heart, lung, pancreas, kidney, liver, intestine, muscle, connective tissue, bone, cartilage, or blood). Biopsy samples may be obtained invasively, non-invasively, or minimally invasively. In some cases, the biopsy sample is obtained from one or more active lesions, dormant lesions, or normal tissue.

In some cases, once removed from the skin, the device and extracted RNA biomarker may be placed in a storage buffer, a transport buffer, or an assay buffer. The device and extracted RNA biomarker can be stored at-80 ℃, -20 ℃, -4 ℃, 4 ℃ or room temperature. Alternatively, the device may be placed in a buffer to dissociate the extracted RNA biomarker from the device, and the extracted RNA biomarker may be stored at-80 ℃, -20 ℃, -4 ℃, or room temperature. The extracted RNA biomarker (with or without the device) may be sent to a laboratory for further analysis.

In some cases, DNA is sequenced using nanopore sequencing, where individual DNA or RNA molecules can be sequenced without PCR amplification or chemical labeling of the sample. In some cases, the extracted nucleic acids of the sample may be amplified and analyzed by Next Generation Sequencing (NGS), high throughput sequencing, massively parallel sequencing, or sequencing-by-synthesis. Different sequencing methods may include, for example, pyrosequencing, sequencing by reversible terminator chemistry, ligase mediated sequencing-by-ligation or phosphorus-ligated fluorescent nucleotides or real-time sequencing. Methods for performing genomic analysis may also include microarray methods. In some cases, genomic analysis may be used in combination with any other method herein. For example, a sample may be obtained, tested for sufficiency, and divided into aliquots. One or more aliquots may then be used in the cytological assays of the invention, one or more may be used in the gene expression profiling methods of the invention, and one or more may be used in genomic analysis. It is also to be understood that the present invention contemplates that one skilled in the art may wish to perform other analyses on biological samples not explicitly provided herein.

In some embodiments, wherein the nucleic acid molecule is RNA, the method further comprises converting the captured RNA into DNA (e.g., cDNA) that is readily available for sequencing (e.g., sequencing-by-synthesis or nanopore sequencing). In some cases, RNA extraction was performed using the RNeasy Mini kit (Qiagen, valencia, CA). Genomic DNA can be removed from RNA preparations using DNase-free DNase digestion (Qiagen, valencia, calif.). Reverse transcription can be performed using Taqman kit (Applied Biosystems, foster City, calif.). For example, 200ng of total mRNA was added to 20. Mu.l of the reverse transcription reaction, and the mixture was incubated at 25℃for 5 minutes, at 48℃for 30 minutes, and then at 95℃for 5 minutes. The cDNA was stored at-20℃for further use.

In some cases, the cDNA may be amplified and then sequenced by commercial suppliers (psomine, inc., rockville, MD) according to standard procedures. Library preparation can be accomplished using Illumina Nextera DNA Flex kit according to manufacturer's instructions. The prepared index library can be loaded onto NovaSeq 6000S 4, with a read length of 150PE for sequencing of 40M reads per sample. During sequencing, the mass fraction (Q30) may be maintained at a certain level, for example above 75%. After the run is complete, the FASTQ file quality can be checked with the FASTQC and trimmed with trim_gain program. The trimmed FASTQ may be aligned and mapped to a human reference genome (e.g., GRCh 38) using the hisat2 program. In some embodiments, the number of reads per Ensemble gene ID is counted using the FeatureContts program and the Chile GRCH38.84. Gtf. In some cases, RNA expression analysis was further processed using a Bioconductor package edge. The filterByExpr filter genes can be used before the logCPM (counts per million reads) is calculated. The plots can be made in R with ggplot2 and hetmap.2 functions. The ratio of detected genes (with a count > 0) to the total number of Ensemble genes can be used to determine the percentage of genes detected.

General methods for determining the level of a gene expression product may include, but are not limited to, one or more of the following: additional cytological assays, assays for specific protein or enzyme activity, assays for specific expression products (including protein or RNA or specific RNA splice variants), in situ hybridization, whole or partial genome expression analysis, microarray hybridization assays, SAGE, enzyme linked immunosorbent assays, mass spectrometry, immunohistochemistry, or blotting. The level of gene expression product may be normalized to the level of expression of an internal standard, such as total mRNA or a particular gene (including but not limited to glyceraldehyde-3-phosphate dehydrogenase or tube protein).

In some embodiments, microarray analysis begins with extracting and purifying nucleic acids (e.g., biopsies or fine needle aspirates) from a biological sample using methods known in the art. For expression analysis, it may be advantageous to extract and/or purify RNA from DNA. It may be further advantageous to extract and/or purify mRNA from other forms of RNA, such as tRNA and rRNA.

In some embodiments, the purified nucleic acid may also be labeled with a fluorescent, radionuclide, or chemical label such as biotin or digoxin, for example, by reverse transcription, PCR, ligation, chemical reaction, or other techniques. The marking may be direct or indirect, which may further require a coupling stage. The ligation stage may occur prior to hybridization, for example using aminoallyl-UTP and NHS amino-reactive dyes (e.g., cyanine dyes) or after hybridization, for example using biotin and labeled streptavidin. Modified nucleotides (e.g., at a ratio of 1 aaUTP:4 TTP) are added enzymatically at a lower rate than normal nucleotides, typically yielding 1 base per 60 bases (measured with a spectrophotometer). The aaDNA may then be purified using, for example, a chromatographic column or a diafiltration device. An aminoallyl group is an amine group attached to a long linker of a nucleobase that reacts with a reactive label (e.g., a fluorescent dye).

In some embodiments, the labeled sample may then be mixed with a hybridization solution, which may contain SDS, SSC, dextran sulfate, a blocking agent (such as COT1 DNA, salmon sperm DNA, calf thymus DNA, poly a or poly T), a Denhardt solution (Denhardt's solution), formaldehyde amine, or a combination thereof. Hybridization probes are variable length DNA or RNA fragments that are used to detect the presence or absence of a nucleotide sequence (DNA target) in a DNA or RNA sample that is complementary to a sequence in the probe. Thus, the probe hybridizes to a single stranded nucleic acid (DNA or RNA) and its base sequence allows probe-target base pairing due to complementarity between the probe and the target. The labeled probe is first denatured (by heat or under alkaline conditions) into single DNA strands and then hybridized to the target DNA.

In some embodiments, to detect hybridization of a probe to its target sequence, the probe is tagged (or labeled) with a molecular marker; the usual markers are ³² P or digoxin, digoxin is a non-radioactive antibody-based marker. Then by autoradiography or other meansImaging techniques visualize hybridization probes to detect DNA sequences or RNA transcripts with moderate to high sequence similarity to the probes. Detection of sequences with moderate or high similarity depends on the stringency of the application of hybridization conditions-high stringency, such as high hybridization temperature and low salt in hybridization buffer, allowing hybridization between only highly similar nucleic acid sequences, and low stringency, such as low temperature and high salt, allowing hybridization when sequences are less similar. Hybridization probes used in DNA microarrays refer to DNA that is covalently attached to an inert surface (such as a coated slide or gene chip) and to which a mobile cDNA target hybridizes.

In some embodiments, the mixture may then be denatured by heat or chemical means and added to ports in the microarray. The wells may then be sealed and the microarray hybridized, for example, in a hybridization oven, wherein the microarrays are mixed by rotation or in a mixer. After overnight hybridization, non-specific binding (e.g., with SDS and SSC) can be washed away. The microarray can then be dried and scanned in a special machine, where the laser excites the dye and the detector measures its emission. The image may be overlaid with a template grid and the intensity of the feature (several pixels making up a feature) may be quantified.

Various kits may be used for nucleic acid amplification and probe generation for the subject methods. Examples of kits that may be used in the present invention include, but are not limited to, the Nugen WT-Ovation FFPE kit, cDNA amplification kits having a Nugen exon module and a fragment/Label module. NuGEN WT-Ovation ^TM FFPE system V2 is a complete transcriptome amplification system that enables global gene expression analysis of a large number of small and degraded RNA profiles derived from FFPE samples. The system includes reagents and protocols required to amplify total FFPE RNA as low as 50 ng. The protocol can be used for qPCR, sample archiving, fragmentation and labeling. Amplified cDNA can be fragmented and labeled in less than two hours for use with NuGEN's FL-variation ^TM cDNA Biotin Module V23' expressionAnd (5) array analysis. For using AffymetrixAnalysis of exon and Gene ST arrays amplified cDNA can be used with WT-Ovation exon modules followed by fragmentation and FL-Ovation ^TM cDNA biotin module V2 labeling. For analysis on an Agilent array, the amplified cDNA can be fragmented and NuGEN's FL-Ovation used ^TM The cDNA fluorescent module is marked.

In some embodiments, an Ambion WT expression kit may be used. The Ambion WT expression kit allows for direct amplification of total RNA without a separate ribosomal RNA (rRNA) removal step. UsingWT expression kit, can be in-> Samples of total RNA as little as 50ng were analyzed on human, mouse and rat exons and genes 1.0ST arrays. Except for lower input RNA requirements and +.>Method and->In addition to the high consistency between real-time PCR data, < > on>The WT expression kit also significantly increased sensitivity. For example, use +.>WT expression kits can obtain a greater number of probes detected above background at the exon levelA group. The Ambion WT expression kit may be used in combination with an additional Affymetrix labeling kit. / >

In some embodiments, the Amptec trinucleotide nano mRNA amplification kit (6299-A15) may be used in the subject methods.Trinucleotide mRNA amplification nanokits are suitable for a wide range of 1ng to 700ng of total input RNA. Depending on the amount of total RNA input and the desired aRNA yield, it can be used for 1 round (input>300ng total RNA) or 2 rounds (minimum input of 1ng total RNA), where aRNA yield is at>In the range of 10. Mu.g. The AmpTec-specific trinucleotide priming technique can preferentially amplify mRNA (independent of the universal eukaryotic 3' -poly (a) -sequence), combined with selection for rRNA. The kit can be used in combination with a cDNA converting kit and an Affymetrix labeling kit.

In some embodiments, the raw data may then be normalized, for example, by subtracting the background intensity, and then dividing by the intensity, the total intensity of the features on each channel is equalized or the intensities of the reference genes are equalized, and then the t-values for all intensities may be calculated. More complex methods include the z-ratio of Affymetrix chips, the loess and lowess regression, and RMA (robust multichip analysis).

In some cases, the poly-a tail mRNA is captured on the support via hybridization to a poly-T DNA capture probe or primer coupled to the surface of the support. The poly-T strand is then extended with reverse transcription polymerase to produce a double stranded molecule comprising DNA: RNA double strands. Next, a transposome complex (e.g., tn5 transposase bound to an adaptor sequence and a sequence complementary to a surface amplification primer) is added to the support, which translocates and labels the duplex, thereby ligating the DNA adaptor oligomer to the 5' end of the RNA strand. The 3 'end of the DNA strand can then be extended using a strand displacement polymerase (e.g., bst polymerase) to displace the untransferred strand of the transposome complex and copy the RNA strand to its 5' DNA chimeric end. In some embodiments, the DNA adapter oligomer comprises a sequence complementary to an anchor primer on a substrate configured for sequencing. In some embodiments, the DNA adapter oligomer comprises a sequence complementary to a sequencing primer used for sequencing. The double-stranded molecules can then be amplified (e.g., cluster amplified) and sequenced using sequencing primers. The primer portion comprises an adapter sequence and an upstream adapter sequence. Alternatively, the other end of the molecule (poly-T end) can be sequenced with a primer that anneals upstream of the poly-T sequence and is extended with natural dATP nucleotides before starting the sequencing-by-synthesis (SBS) chemical cycle.

In some cases, the RNA is fragmented and treated with a phosphatase. A single stranded adapter molecule is ligated to the 3' end of each RNA fragment containing the complement of the surface binding primer. Fragments are then added to the support and captured via hybridization. The hybridized RNA molecules are converted to DNA-RNA duplex using reverse transcription polymerase. A transposome complex or composition comprising a transposase and an adaptor duplex (i.e., a transposon) of ME and P5 is used to label the duplex. After extension of the DNA strand to the end with a strand displacement polymerase, the molecule can be amplified (e.g., cluster amplified) and sequenced.

Bioinformatics analysis of sequence reads

As used herein, a Positive Predictive Value (PPV) is the percentage of true positives in all subjects tested positive. It can be calculated as follows: ppv=tp/(tp+fp), where TP and FP are the number of true and false positive results, respectively.

As used herein, negative Predictive Value (NPV) is the percentage of true negatives in all subjects tested negative. It can be calculated as follows: ppv=tn/(tn+fn), where TN and FN are the number of true and false negative results, respectively.

An algorithm (Mind. Px) was developed herein for predicting the response to biological agents (anti-IL-17, anti-IL-23) for treating patients with psoriasis by comparing baseline transcriptomes to clinical responses to biological agents 12 weeks after initial treatment. 62 patients who developed responses (75 total) had psoriasis area and severity index changes of 0.75 or greater, while non-responders had Psoriasis Area and Severity Index (PASI) changes of-0.2 to 0.75. Cross-validation using linear regression modeling showed that using PASI 75 as the cutoff for evaluating classifier performance resulted in a balanced prediction accuracy of 0.71. A positive predictive value of 0.95 was achieved.

In some cases, sequence reads from a subject are compared to sequence reads from positive or negative controls. A positive control is a subject diagnosed as having a disease or condition. The negative control is a healthy person without symptoms or history of symptoms of any disease or condition. Alternatively, the negative control may be a baseline for the subject prior to treatment.

Biological agent

As used herein, the term "biological agent" means a pharmaceutical product obtained from a living organism (e.g., a human, animal, or microorganism), including recombinant biological agents produced by genetic engineering techniques. For example, biological agents may contain proteins that control the effects of other proteins and cellular processes, genes that control the production of important proteins, modified human hormones, or cells that produce substances that inhibit or activate components of the immune system. In some cases, the biological agent is an antibody (e.g., a monoclonal antibody). In some cases, the subject is treated with one or more of the following biological agents: tumor necrosis factor inhibitors (e.g., adalimumab, etanercept, infliximab, golimumab (golimumab) and polyethylene glycol conjugated cetuximab); IL-17 inhibitors (e.g., secukinumab, iximab Bei Shan antibody, and bromoxynil); and IL-23 inhibitors (e.g., ti Qu Jizhu mab, antique-ku mab, wu Sinu mab, and Li Sanji bead mab).

Whenever the term "at least", "greater than" or "greater than or equal to" precedes the first value in a series of two or more values, the term "at least", "greater than" or "greater than or equal to" applies to each value in the series. For example, 1, 2, or 3 or more is equivalent to 1 or more, 2 or 3 or more.

Whenever the term "no greater than", "less than" or "less than or equal to" precedes the first value in a series of two or more values, the term "no greater than", "less than" or "less than or equal to" applies to each value in the series of values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

As used herein, the term "base" refers to a nucleotide. In some cases, a "base" may refer to a base pair ("bp"), e.g., 1 base equals 1 base pair. As used herein, the terms "base" and "base pair" are used interchangeably.

As used herein, the term "about" means within 10% above or below a given value. For example, "about 10" will include values from 9 to 11 unless the context in which the term is used indicates otherwise.

The following examples are provided to further illustrate the advantages and features of the present invention and are not intended to limit the scope of the invention. Although they are exemplary procedures, methods, or techniques that may be used, other procedures, methods, or techniques known to those skilled in the art may alternatively be used.

Examples

Example 1: manufacturing apparatus

Injection molding is used to manufacture the devices of the present disclosure. According to features disclosed herein, a device is manufactured by injecting a heated (e.g., molten) material, such as the polyolefin resins disclosed herein, into a mold of the device. The mold includes a plurality of cavities for forming a plurality of microneedles; a cavity for forming an interior portion comprising a plurality of microneedles; and one or more cavities for forming one or more peripheral portions adjacent the inner portion. For this device, the width of the inner portion is less than the width of the one or more outer peripheral portions. The ratio of the width of the inner portion to the width of the one or more outer peripheral portions is about 1:5.

The smaller width of the inner portion (as compared to the one or more outer peripheral portions) facilitates movement of the heating material into the one or more cavities as the heating material is injected through the mold to create the plurality of microneedles prior to creating the inner portion and the one or more outer peripheral portions.

The result is a device as described herein that includes uniformly sharp microneedles. No more than 3% of the microneedles in the plurality of microneedles deviate (+/-) by more than about 50 μm from the average length of the microneedles in the plurality of microneedles.

Example 2: probe combined with the device of example 1

The microneedles of the device of example 1 were plasma treated as described herein. The apparatus, or a portion thereof, is placed in a plasma vacuum chamber, the plasma vacuum chamber is closed, thereby creating a sufficient vacuum seal, all pre-existing gas present in the chamber is evacuated, and plasma processing gas is pumped into the plasma vacuum chamber to a defined pressure, thereby enabling the generation of a gas plasma.

The gas plasma reacts with the heated material of the device (e.g., the polyolefin resins described herein) and makes the material more reactive to form covalent bonds with the probes described herein.

Example 3: kinetics of mRNA extraction

The optimal time for mRNA extraction was determined by placing the microneedle device described herein on the skin of a healthy subject for various periods of time, followed by removal of the patch from the skin and subsequent amplification, cDNA synthesis, and qPCR analysis. The device was placed on a subject for 20 seconds, 1 minute, 5 minutes, and 10 minutes, and the cycle threshold (Ct) of two biomarkers GAPDH and KRT10, which were generated from mRNA samples collected from the device at each residence time, were measured. All measurements were performed in triplicate.

As can be seen in table 1, the best extraction occurs when there is 5 minutes of residence in the skin, with degradation beginning to dominate at 10 minutes of skin insertion.

Example 4: extraction of mRNAIs of (2)

To demonstrate the quality of intact mRNA, bioanalyzer QC and qPCR analysis were performed with mRNA extracted from the microneedle devices described herein.

For complete/high quality mRNA, successful cDNA synthesis and amplification showed a distinct amplification peak spanning about 300 to about 10,000bp, with the peak at about 1,500bp (FIG. 5A). Quantification of the area under these curves yields a corrected area QC ratio (corrected area for large mRNA fragments (700-20,000 bp)/corrected area for small mRNA fragments (150-200 bp)) of greater than 1. In contrast, the amplification peak shifted with degraded mRNA to smaller fragments of 100-1,000 in the partially degraded sample (fig. 5B). Furthermore, in the fully degraded sample, the peak around 1,500bp was no longer visible (fig. 5C). Importantly, the corrected area QC ratio was less than 1 in these partially and fully degraded samples. In addition, qPCR analysis was performed on these samples. Using the classical gene (B2M), the Ct value from the degraded RNA sample increased from 19.25Ct to 22.70 for the control sample (whole RNA); this 3.45Ct increase indicates an approximately 11-fold decrease in the amount of mRNA in the degraded sample.

Example 5: prospective prediction of individual patient responses to TNFa, IL-17 and IL-23 inhibitors

Prior to administration of a particular drug class (e.g., TNF alpha, IL-17i, and IL-23i inhibitors), the response of patients with psoriatic skin disease to the drug class is prospectively determined via predictive linear classifier modeling methods. To determine patient response, the entire RNA transcriptome of an individual patient is pulled by exposing the skin biomarker patch and the patient's skin for 5 minutes using an FDA-registered painless class I skin biomarker patch. The skin biomarker patch is then treated and RNA that adheres to the skin biomarker patch is extracted and sequenced using next generation sequencing. RNA sequencing reads are then aligned and compared to a database of patients with similar disease condition RNA gene profiles and their respective responses or no responses to specific drug classes. Comparison between RNA sequencing reads with the predictive linear classifier and the database provides a probability score that a particular drug or drug class will provide an effective response to treating a disease condition.

Using the above method, the predictive linear classifier Positive Predictive Value (PPV), sensitivity and balance accuracy were evaluated for the TNF. Alpha.i, (PASI75@W12), IL-17i (PASI75@W12) and IL-23i (PASI150@W12) drug classes. The tnfi inhibitor drug class was evaluated in a training group of 34 patients and a test group of 15 patients. The resulting PPV, sensitivity and balance accuracy of the linear classifier were 89%, 80% and 80%, respectively. The IL-17i inhibitor drug class was evaluated in a training group of 75 patients and a test group of 6 patients. The resulting PPV, sensitivity and balance accuracy of the linear classifier were 100%, 100% and 100%, respectively. The IL-23i inhibitor drug class was evaluated in a training set of 17 patients and a test set of 17 patients. The resulting PPV, sensitivity and balance accuracy of the linear classifier were 90%, 82% and 83%, respectively. The average of PPV, sensitivity for the three inhibitor drug classes was calculated as 95% PPV, 87% sensitivity and 88% specificity.

Example 6 machine learning based test for predicting response to psoriasis biologicals

This study was aimed at developing and prospectively validating a machine learning-based algorithm that could predict patient responses to the most common biopharmaceutical categories in psoriasis patient management. This type of tool will give the clinician more confidence that a given patient will respond to a particular class of medication, thereby improving health and reducing wasteful healthcare expenditures.

Patients were enrolled into one of two observational studies (STAMP study) where a skin biomarker patch (DBP) was applied at baseline prior to drug exposure followed by clinical evaluation 12 weeks after exposure. PASI measurements were performed at baseline and 12 weeks to evaluate clinical response to clinical phenotypes. Respondents were defined as those who reached PASI 75 at 12 weeks. Transcriptomes obtained from DBP were sequenced and analyzed to derive and/or validate the classifier for each biological class, which was then combined to generate predicted responses for all three biological drug classes (IL-23 i, IL-17i and tnfαi).

These studies recruited a total of 242 psoriasis patients, including 118 patients treated with IL-23i (49.6%), 79 patients treated with IL-17i (33.2%), 35 patients treated with TNF alpha i (14.7%), and 6 patients treated with IL-12/23i (2.5%). IL-23i predictive classifiers were developed from early recruitment of patients and validated independently with later recruited patients. IL-17i and TNFαi predictive classifiers were developed using publicly available datasets and validated independently with patients from STAMP studies. In independent validation, the positive predictive values for the three classifiers (IL-23 i, IL-17i and TNFαi) were 93.1%, 92.3% and 85.7%, respectively. Throughout the cohort, 99.5% of patients were predicted to respond to at least one drug class.

This study demonstrates the ability to use baseline skin biomarkers and machine learning methods as applied to predict psoriasis biomarkers prior to drug exposure. Using this test, patients, physicians, and healthcare systems can all benefit in different ways. Accurate medical treatment can be achieved for individual patients because most patients may respond to their prescribed biological agents for the first time. Doctors can prescribe these drugs more confidently, and healthcare systems will achieve lower net costs and greatly reduce wasteful expenditure by significantly increasing the initial response rate to expensive biotherapeutic agents.

Psoriasis is a T cell mediated inflammatory skin disease characterized by discrete erythema plaques and papules with mica-like scales. Worldwide, this is a common disease, and about 2.8% of the U.S. population, 750 ten thousand, is diagnosed with psoriasis. The current treatment modality for psoriasis is characterized by local drug treatment and/or phototherapy for mild to moderate patients, and systemic drug treatment for patients classified as moderate to severe disease. The advent of biological therapy as one of these systemic agents has drastically altered the management and treatment of psoriasis patients and is a direct consequence of increased molecular understanding of the disease. Currently, there are 11 batches of biological agents available in the united states for the treatment of psoriasis, and more are under development. However, it is currently unclear which biological agents will be effective for a given patient.

Disclosed herein is a novel biomarker capture platform that utilizes a skin biomarker patch to capture full transcriptomes including mRNA biomarkers from the epidermis and upper dermis. This platform shows excellent consistency with biopsies and provides an extensible method to obtain skin biomarkers in a minimally invasive manner. It is contemplated that the use of this platform in a preliminary machine learning classifier can predict responses and non-responses to IL-17 and IL-23 inhibitors. An extension of this preliminary study is also disclosed herein to develop and prospectively verify an operable clinical test for predicting patient responses to all three drug classes of psoriasis biologicals.

Method

Skin biomarker paster platform

The skin biomarker patch (DBP) used in this study was manufactured and modified as previously described and used according to the manufacturer's instructions.

Human subject recruitment and recruitment

Data were analyzed from past and ongoing observational, multicenter (20 centers), single arm, open label, 12 week studies (known as STAMP studies). The protocols of these studies were approved by the local institutional ethics committee and met the guidelines of the "helsinki statement" and the international coordination center (ICH) Good Clinical Practice (GCP) guidelines. All patients receiving treatment provided written informed consent. The main objective of the study protocol was to examine whether baseline or in-treatment transcriptomics could be used to help predict drug treatment options and provide new therapeutic targets for drug development (table 2). Visit included screening, baseline, week 1, week 4, week 8, and week 12. PASI, PGA, and BSA scores were performed at each visit, excluding screening visits. The subject was applied a skin biomarker patch at each visit, excluding the screening visit. Medical history, physical examination, and demographic data of the subject are collected at the time of screening.

Table 2. Schedule of stamp study activity.

Abbreviations: BMI = body mass index; IP = study product; PASI = psoriasis area and severity index; PGA = physician global assessment; bsa=body surface area.

¹ Medical history includes prescription and over-the-counter medical history.

² Is only applicable to subjects who have not been examined by rheumatologists or dermatologists within 30 days prior to screening. Including height, weight, and BMI.

³ If the screening and baseline are performed on the same day, the clinician must ensure that the subject avoids using any local steroid 2 weeks before applying the Mindera skin biomarker patch.

⁴ For screening/baseline assessment, physical examination, PASI, PGA, BSA, and/or Mindera skin biomarker patch application may be completed at the screening visit or baseline visit (PASI, PGA, and BSA should be completed in the same visit and prior to Mindera skin biomarker patch application).

Study population

These studies recruit male and female patients aged 18 years or older, diagnosed by rheumatists or dermatologists as having psoriasis, with at least one identifiable study lesion of 2cm in diameter or greater, and are scheduled to be treated with IL-23 inhibitor (IL-23 i), IL-17 inhibitor (IL-17 i) or tnfα inhibitor (tnfα i) therapy once recruited into the study. Exclusion criteria included the use of topical steroids for the study lesions within 2 weeks prior to baseline access, along with the use of hydrochloroquin sulfate tablets (Plaquenil). All study participants were also instructed to avoid using all local steroids throughout the study, until the study treatment ended.

Skin biomarker patch application

To apply the DBP to the skin, a custom spring-loaded applicator is used. This applicator is used to normalize the applied pressure of the subject and the user. The loading applicator is placed on the skin and the trigger is depressed, thereby applying the patch to the skin. The patch was then held on the skin with a loop of medical tape for 5 minutes. After this, the patch was removed from the subject, immediately placed in a storage buffer (LiCl, triton X-100, tris-EDTA) and stored at 4 ℃ until treatment.

Skin biomarker patch treatment

Skin transcriptomes were treated within 96 hours after collection from the subject. The applied DBP was washed with cooled 1XPBS and then dried under a nitrogen flow. mRNA was extracted from the patch by applying PCR grade water (50. Mu.L, 95 ℃) to DBP. The patch was then heated at 95 ℃ for 1 minute to elute the bound mRNA from the DBP. Takara is then used according to the manufacturer's instructionsThe single cell kit converts this eluted mRNA to cDNA. Amplified cDNA samples were then stored at 4℃until analysis.

Next generation sequencing program

Amplified cdnas were sequenced by commercial suppliers (psinage, inc., rockville, MD) according to standard procedures. Library preparation was accomplished using Illumina Nextera DNA Flex kit according to manufacturer's instructions. The prepared index library was then loaded onto NovaSeq 6000S 4, with a read length of 150PE for 40M read sequencing per sample. During sequencing, the mass fraction (Q30) remained above 75%. After the sequencing run was completed, the FASTQ file quality was checked with FASTQC and trimmed with trim_galore program. The trimmed FASTQ files were aligned and mapped to the human reference genome GRCh38 using the hisat2 program. The number of reads per Ensemble gene ID was counted using the FeatureContts program and the Chile GRCH38.84. Gtf. RNA expression analysis was further processed using a Bioconductor package edge. The genes were filtered using a filterByExpr before logCPM (log count per million reads) was calculated as a measure of gene expression levels. For downstream classifier construction, logCPM values are used.

IL-23 classifier development

5 common classifiers were selected and used to predict responders to IL-23i treatment using the R-program package caret. Selected classifiers have been frequently used in the medical field for exploring predictive or prognostic biomarkers and include glrnet (lasso and elastic network regularization generalized linear model), PAM (nearest shrink centroid method), LM (linear regression model), SVM (support vector machine) and RF (random forest).

The predictive performance of 5 classifiers was compared using the following experimental design: 1) An outer fold that separates the dataset into ten layers; 2) For each fold, the data is pre-processed for feature selection. The first 20, 50 or 200 differentially expressed genes (signatures) were selected using a linear regression model; 3) Adjusting the hyper-parameters in the training set via ten fold cross-validation, and then repeating the process five times; 4) Based on the selected hyper-parameters, a model is derived from the training set and applied to the test set. The performance index of the test set is then calculated. This process was repeated five times for each classifier.

The earliest recruited IL-23i treatment patients in the STAMP study were used for IL-23i classifier training. Baseline PASI filters (none, 6+, 8+ and 10+) were applied to explore the impact of disease severity on classifier performance. Classifier training was performed using the machine learning method described above, and 10-fold cross-validation was used to evaluate test performance. Once the desired performance (> 85% PPV and >85% sensitivity) is achieved, the IL-23i classifier is locked. Patients were treated with IL-23i recruited after classifier locking as independent validation set.

IL-17 classifier development

A list of 17 genes for predicting the response of psoriatic patients to IL-17i is disclosed herein, together with a publicly available dataset by analysis. Briefly, patients with moderate to severe psoriasis (baseline PASI. Gtoreq.10) were treated with bromoantibody and were followed up for 12 weeks. PASI measurements were made at baseline and week 12, and patients were assessed for therapeutic response using week 12 PASI 75. Lesion and non-lesion skin biopsy samples were collected at baseline and week 12. RNA profiling was performed using the Affymetrix microarray platform. Lesion samples collected at baseline were used in predictive biomarker analysis.

The 17 predicted genes were inversely related to the patient's response to bromobudadizumab. The 17 genes were mapped to 14 Ensemble gene IDs reported in RNASeq data from STAMP studies. 14 gene classifiers were validated in the STAMP study.

TNFa classifier development

Publicly available datasets in NCBI gene expression complex (GEO) database (https:// www.ncbi.nlm.nih.gov/GEO /) and European bioinformatics institute (EMBL-EBI) big data database (https:// www.ebi.ac.uk /) were used as classifier training datasets. For initial data selection, search terms for psoriasis patients with biological agent treatment and transcriptome profiling were used to identify array or sequencing data.

The supervised predictive biomarker selection is applied to individual training data to filter genes based on the following assessments: 1) Correlation between gene expression and patient response; 2) Median gene expression level; 3) Dynamic range of gene expression; 4) Differences between average gene expression in responders and non-responders. The ratio of down-regulated genes and up-regulated genes in tnfi responders was used to develop a prediction of tnfi therapeutic response.

Prospective classifier validation

IL-23i, IL-17i and TNFαi classifiers were independently validated using patients enrolled in the STAMP study. Each classifier discretely predicts whether the patient is a responder or a non-responder to the biological agent class. The response is defined as achieving PASI 75 at week 12. Cross tabulation of observation and prediction categories and related statistics is calculated using the confusionMatrix function of the R caret package.

(a) Characteristics of the study object

At the time of data locking, a total of 242 psoriasis patients were enrolled in the STAMP study (fig. 7), including 38 patients still in follow-up. Stanp is an active recruitment study aimed at continuing to recruit new patients to support psoriasis biomarker studies. Different demographics and clinical characteristics of the subjects were observed (table 3). In terms of drug class, 49.6% of patients were treated with IL-23i, 33.2% with IL-17i, 14.7% with TNFαi, and 2.5% with IL-12/23 i.

Table 3.Stamp study patient demographics and disease profile.

/>

The values are n (%) or mean ± standard deviation of the patient

Of the 242 patients initially identified, 185 patients completed the study, meaning baseline and week 12 PASI scores were collected, and 57 patients failed screening, were out of visit, or were still in visit. Of 185 patients, 177 had baseline DBP samples collected, whereas 8 had failed to collect DBP samples. In this subset, 167 samples passed the sequencing data QC index and were included in the biomarker analysis, with 10 (5.4%, 10/177) samples failing to pass the sample processing or sequencing data QC.

The patient response rate for the whole cohort was 64.1% and the response rate for the different biologicals ranged from 47.6% to 72.5% (table 4). In all drug classes, the response rate was 26.4% higher in patients with high (baseline PASI. Gtoreq.8) PASI than in patients with low PASI.

Table 4. Psoriasis patient response rate.

High (baseline PASI > 8) PASI patients were used for predictive classifier development and validation. The IL-23i treated patient population was divided into two subsets, 17 IL-23i treated patients were used to train the IL-23i predictive classifier, and the remaining 43 patients were used for prospective validation. All high PASI IL-17i and TNF alpha i patients were used for classifier validation.

(b) IL-23i classifier development and performance in training set

A subset of 17 IL-23i treated high PASI (. Gtoreq.8) patients were used for IL-23i predictive classifier training, including 9 responders and 8 non-responders. The best performing model was constructed on glrnet using the first 50 features selected by the linear regression model. Test performance was assessed by ten fold cross validation and Positive Predictive Value (PPV), sensitivity and balance accuracy were 89.7%, 96.3% and 91.9%, respectively.

TNFa data sources and predictive biomarker discovery

Four publicly available datasets were identified (table 5) and used for tnfα i response classifier development. These datasets included a total of 73 patients, of which 58 had transcriptome data and outcome assessment data for predicting biomarker findings. Patient outcome was assessed with either the PASI 75 or histological response at week 12 or week 16.

Table 5. Publicly available datasets for tnfa i classifier training.

The supervised predictive biomarker selection is applied to four training data sets. Nine genes were identified as predictive of tnfαi responses in at least two data sets (table 6). The output of the classifier is the tnfa i response predictive score; in this scoring system, the lower the predictive score, the higher the probability that the patient will respond to tnfαi treatment. Classifier performance showed PPV, sensitivity and balance accuracy within this training set to be 78.9%, 43.1% and 63.7%, respectively (table 7).

TABLE 6 list of 9 genes identified as being associated with response to anti-alpha biologicals

Gene name	Gene symbol
		Keratinized envelope protein cutin silk	CNFN
Cathepsin C	CTSC
		Glucoamidoyl beta pseudogene 1	GBAP1
Cell retinoic acid binding protein 2	CRABP2
		Tropocadherin 7	PCDH7
Peptidyl prolyl isomerase G	PPIG
		Rab-31, a Ras related protein	RAB31
Complement component 3	C3
		Early growth reaction protein 1	EGR1

Table 7. Test performance of tnfαi classifier on training set.

Sample amount	58
		Number of respondents	34
Number of non-respondents	24
		PPV	78.9％
NPV	51.3％
		Sensitivity of	44.1％
Specificity (specificity)	83.3％
		Balance accuracy	63.7％

Mind.Px classifier validation

Patient demographics and disease characteristics of 95 patients included in the prospective validation can be found in table 8. Only patients with baseline PASI.gtoreq.8 are included in the trial. For the three classifiers, the range of positive predictive values was 85.7% to 93.1% (table 9). The correlation between observed W12 PASI changes and predicted drug responses was evaluated (fig. 8A-C).

TABLE 8 classifier validation set patient demographics and disease features

Table 9. Classifier validation test performance of three classifiers for patients with baseline PASI > 8.

The same analysis was repeated for 66 moderately to severely ill patients (i.e., PASI. Gtoreq.10) and similar overall test performance was observed, with PPV ranging from 90% to 100% in this smaller cohort (Table 10).

Table 10. Three classifiers verify test performance for patients with a baseline PASI of > 10.

Patients with baseline PASI <8 were also analyzed to determine classifier performance in lighter patients. In this case, the balance accuracy of the three classifiers ranged from 44.4% to 52.8%, indicating that the developed classifier was optimized for moderate to severe psoriasis patients. Mind.Px predictive response prevalence

The predicted response incidence to patients for all three classifiers (IL-23 i, IL-17i and TNF. Alpha. I) was assessed using 195 patients with baseline DBP samples and completed RNAseq sequencing data (FIG. 9 and Table 11). Individually, the predicted response rates for IL-23i, IL-17i and TNFαi classifiers were 72.3%, 51.7% and 67.1%, respectively. It is crucial that 99.5% (194/195) of patients are predicted to be responders to at least one of the three drug categories. All possible combinations of three drug categories appear as having 17.4% (34/195) of patients predicted to be respondents for all three drug categories, 56.9% (111/195) of patients predicted to be respondents for two of the three drug categories, and 25.1% (49/195) of patients predicted to be respondents for one of the three drug categories.

Table 11. Mind.px predicted response incidence for patients enrolled in the stamp study.

STAMP study demographics

242 patients were included in this analysis, whose demographics were substantially identical to those of the previous study in terms of gender, race and age (Table 3). Similarly, the average patient in these studies was obese (BMI > 30), with an average age of 48.5 years. Most interestingly, in this study, the vast majority of patients (86%) either used the biologic for the first time, or had not been administered for the past 12 weeks. This finding is particularly surprising given that many moderate to severe psoriasis patients have been exposed to biological agents, but analysis of the classifier response of the first-used biological agent versus biological agent-exposed patients shows that there is no difference in the predictive value of the algorithm in these patient groups.

Classifier development and validation

The final IL-23i classifier was developed and validated using patients enrolled in the STAMP study. A subset of total IL-23i enrolled patients was used as a training set, and the remaining patients in the cohort were used for classifier validation. Because the training set and the test set are from the same study, the samples and data are processed in the same manner, the classifier developed with the training set can be applied to the test set without additional normalization.

The strategy for the development of IL-17i and TNFαi classifiers is different from that of IL-23 i. The sample size of IL-17i and tnfa i patients in the STAMP study was smaller than IL-23i patients and was insufficient to separate into separate training and test sets, so both classifiers were trained using publicly available data sets. It should be noted that the training set from the published data differs significantly from the test set in some respects, including sample collection methods (needle biopsies and skin patches), RNA preparation protocols, transcriptome profiling methods (array and sequencing based). Because of these different properties of the training set, the training set is mainly used only for feature selection. Once the predicted gene is identified, a reduced algorithm using gene expression values or ratios of gene expression values is applied as a prediction classifier. Prior to verification, a cut-off value is preset using percentile data values calculated from the predictive score of the STAMP patient, and this allows for assessment of classifier performance while minimizing the risk of overfitting.

Here, week 12 PASI 75 was used as a determination of patient outcome. In clinical settings where a better response (e.g., PASI 90 or PASI 100) is required, the classifier can potentially be used to identify the group of patients with a certain clinical cutoff adjustment. Further classifier development for definitively authenticating "superresponders" or "supernon responders" is in progress and will be reported at the appropriate time.

All three classifiers were validated for patients with baseline PASI > 8. However, these classifiers have limited predictive value in patients with lower initial PASI scores. This is likely because the three classifiers were developed with high (. Gtoreq.10) PASI patients as training sets to match the type of patients enrolled in the critical clinical study for each biological agent. Mild patients may have biologically different transcriptome biomarkers. Alternatively, in patients with low initial PASI scores, the reliability of the response determination measurement (PASI 75) is lower in view of the reduced dynamic range of the measurement.

Other clinical variables have previously been used to stratify psoriasis patients or have been associated with poor outcome. In particular, BMI and age are reported to have clinical prognostic value in assessing biotherapeutic response. The predicted significance of BMI and age as possible orthogonal input variables in the classifier was tested. However, no improvement in prediction accuracy or positive predictive value was observed by adding either variable as a covariate when exploring the predictive model.

The occurrence of biomarker predictive responses reveals key features of this test. Of the 195 patients tested, only one patient was predicted to not respond to any of the three biopharmaceutical categories. These data are consistent with a widely accepted clinical fact that psoriasis treatment and management has changed tremendously since the introduction of biological agents; almost all patients will respond to one of three categories of biological agents. However, a gap was observed between the biological formulation behavior and the predicted biological formulation class from the test. Although only 14.7% of the recruiters were prescribed tnfa i biologicals (49.6% of the patient cohort) in the observational STAMP study, 67% of the patient population was predicted to respond to this class.

Disclosed herein is an operable machine learning based accurate medical test that can predict the response of a psoriasis patient to a biologic (tnfαi, IL17i or IL23 i) with a high positive predictive value by combining a skin biomarker patch platform with a machine learning method. Interestingly, when the entire patient cohort is examined, nearly all patients are predicted to respond to at least one biological class, highlighting the tremendous efficacy of biopharmaceuticals in treating psoriasis. Using baseline biomarkers in combination with machine learning algorithm development, appropriate biological agents can be prescribed for a given patient at the first time. This test can improve patient outcome while also saving significant costs to the healthcare system. It is envisaged that this test may effectively reduce trial and error of psoriasis biotherapy and provide physicians, patients and payors with powerful tools to bring personalized medicine into the management of psoriasis patients.

Example 7 effective prediction of response to psoriasis biological agents using machine-learned classifier

Psoriasis affects more than 3% of the population in the united states, and treatment with the most commonly used medications can incur a cost of $87,585 to $366,645 per year. Clinical response to treatment typically takes 12 to 16 weeks to be significant, and the efficacy of currently available therapies ranges from 30% to 80%, at least in part because the response to a particular treatment regimen cannot be predicted.

An algorithm (Mind. Px) was developed herein for predicting the response to biological agents (anti-IL-17, anti-IL-23) for treating patients with psoriasis by comparing baseline transcriptomes to clinical responses to biological agents 12 weeks after initial treatment. 62 patients who developed responses (75 total) had psoriasis area and severity index changes of 0.75 or greater, while non-responders had Psoriasis Area and Severity Index (PASI) changes of-0.2 to 0.75. Cross-validation using linear regression modeling showed that using PASI 75 as the cutoff for evaluating classifier performance resulted in a balanced prediction accuracy of 0.71. A positive predictive value of 0.95 was achieved. A total of 17 genes were ultimately determined to correlate with the patient's response to anti-IL-17 biologicals. Alternatively, a prospective classifier was constructed using the same response criteria (n=17) using patients receiving anti-IL-23 biologicals. A total of 27 genes were finalized for this classifier, and the Positive Predictive Value (PPV) for both classifiers was 100%.

Classifiers for biological prediction mainly utilize orthogonal biomarkers and achieve high PPV. Use of Mind. Px produces better outcomes in psoriasis patients and significantly reduces the cost of the healthcare system.

Human subject recruitment and recruitment

Subjects with active psoriasis and lesions >2cm in diameter were recruited. All procedures were approved by the independent institutional review board (Austin TX) and written consent was obtained for all subjects prior to the study. The study was not statistically significant. For each subject, one Mindera skin biomarker patch (DBP) was applied to the diseased skin. The samples were immediately placed in vials with storage buffer and placed at 4 ℃ until processing, following the manufacturer's instructions. Treatment was performed according to Mind. Px kit instructions.

Skin biomarker patch application

To apply the patch to the skin, a custom spring-loaded applicator is used. This applicator is used to normalize the applied pressure of the subject and the user. The loading applicator is placed on the skin and the trigger is depressed, thereby applying the patch to the skin. The patch was then held on the skin with a loop of medical tape for 5 minutes. After this, the patch was removed from the subject, immediately placed in a storage buffer (LiCl, triton X-100, tris-EDTA) and stored at 4 ℃ until treatment.

Skin biomarker patch treatment

Skin transcriptomes were treated within 72 hours after collection from the subject. Samples were prepared by washing the applied DBP with cooled 1X PBS and then drying the patch under a nitrogen flow. The dried DBP was then placed on a heating block preset to 95℃for 1 minute, at which time 50. Mu.L of PCR grade water previously heated to 95℃was applied to the microneedle array for 1 minute to elute the bound mRNA from the DBP. Takara is then used according to the manufacturer's instructionsThe single cell kit converts this eluted mRNA to cDNA. Amplified cDNA samples were then stored at 4℃until qPCR or NGS analysis was performed.

Sequencing procedure

Amplified cdnas were sequenced by commercial suppliers (psinage, inc., rockville, MD) according to standard procedures. Library preparation was accomplished using Illumina Nextera DNA Flex kit according to manufacturer's instructions. The prepared index library was then loaded onto NovaSeq 6000S 4, with a read length of 150PE for 40M read sequencing per sample. During sequencing, the mass fraction (Q30) remained above 75%. After the run is complete, the FASTQ file quality is checked with FASTQC and trimmed with trim_galore program. The trimmed FASTQ was aligned and mapped to the human reference genome GRCh38 using the hisat2 program. The number of reads per Ensemble gene ID was counted using the FeatureContts program and the Chile GRCH38.84. Gtf. RNA expression analysis was further processed using a Bioconductor package edge. The genes were filtered using a filterByExpr before calculating logCPM (counts per million reads). Plots were made in R with ggplot2 and hetmap.2 functions. The ratio of detected genes (with a count > 0) to the total number of Ensemble genes was used to determine the percentage of genes detected.

Bioinformatics program

Mind. Px Algorithm is already inImplemented as an R script on a system (Mountain View, CA), which is a cloud-based DNA sequencing data analysis and management platform. Cut-off points for anti-IL-17 and anti-IL-23 response predictions have been defined and implemented in R-scripts.

Data from three phase 3 randomized clinical trials were collected: AMAGINE-1, AMAGINE-2 and AMAGINE-3. Endpoint was PASI 75 at week 12. Biopsy samples are collected from the lesion. Transcriptome analysis was performed on biopsy samples using an Affymetrix platform. A total of 75 patients, including 62 responders (83%), were treated with bromobudadizumab; and 12 non-respondents (16%). A total of 17 patients enrolled in the Mindera sponsored STAMP (Mindera patch application study) trial (STAMP is an ongoing clinical trial sponsored by Mindera corporation) was also included in the analysis. Recruitment includes patients with psoriasis who are to be treated with anti-IL-17 or anti-IL-23 biologicals. RNA samples are collected from the patient's skin prior to and multiple times after therapy for use in identifying molecular biomarkers that can predict a patient's response to therapy. Baseline samples were collected from all patients.

After sequencing, the RNA-Seq FASTQ file was trimmed with trim_galore program, then mapped to human reference genome GRCh38 using HISAT2 program. The number of reads per Ensemble gene ID was counted using the FeatureContts program and human GRCh38.84. Gtf. RNA expression analysis was further processed using the bioconductor package edge. The genes were filtered using a filterByExpr before calculating logCPM (counts per million reads). The mapping was performed with ggplot2 and hetmap.2 functions compared to the standard R function. The percentage of detected genes was determined using the ratio of detected genes (with counts > 0) divided by the total number of Ensemble genes. An overview of the RNA-Seq analysis is summarized in FIG. 12.

Skin biomarker patch platform data

Disclosed herein are platforms that allow for simple, rapid and painless extraction of RNA from skin using skin biomarker patches. Subsequent next generation sequencing of the extracted RNA allowed us to take genetic and transcriptome snapshots of the skin at the exact moment of the test. This rich patient-specific dataset can then be analyzed by machine learning algorithms to address complex data problems (e.g., predicting appropriate biopharmaceuticals for the patient prior to therapeutic selection and treatment).

Lesion and non-lesion samples were compared with puncture biopsies using Mindera patches from 66 patients in the Mindera database (FIG. 13). The entire transcriptome is analyzed by performing a paired t-test between diseased and non-diseased skin, and then mapping the data with the highest variance (e.g., the best P-value between groups). Importantly, biomarker data between the Mindera patch sample and the biopsy sample are equivalent. The 30 highest variance genes were selected and unsupervised clustering was performed for visualization, showing good differentiation between diseased and non-diseased skin for the same patient.

Also disclosed herein is an algorithm for predicting the response of a patient suffering from psoriasis to a biologic (anti-IL-17, anti-IL-23) treatment. The dataset from Tomalin et al (2020) was used to identify genes whose RNA expression levels correlated with the response of psoriatic patients to anti-IL-17 biologic therapy.

In this cohort, 62 patients who responded (75 total) had a PASI change of > 0.75, while no respondents had a PASI change of-0.2 to 0.75. The dataset was pre-filtered using differentially expressed genes of psoriasis (lesions versus non-lesions) to minimize the chance of overfitting. Cross-validation using linear regression modeling showed that using PASI 75 as the cutoff for evaluating classifier performance resulted in a balanced prediction accuracy of 0.71. Importantly, a positive predictive value of 0.95 was achieved. Using StepAIC (red cell information guidelines) linear regression modeling, a total of 17 genes were ultimately determined to correlate with the patient's response to budadizumab therapy (table 12). The red pool information criterion is an estimator of the out-of-sample prediction error and thus the relative quality of the statistical model for a given dataset. The red pool information criterion estimates the relative amount of information lost by a given model: the less information a model loses, the higher the quality of the model.

TABLE 12 list of 17 genes identified as being associated with response to anti-IL 17 biologicals

A heat map was generated from the expression data of these 17 genes (FIG. 14). Each column is a baseline sample collected from the patient. From left to right, patients are classified by the change in PASI from low to high. The top blue bar shows 12 non-respondents (W12 PASI change < 0.75); yellow bars show 62 respondents (W12 PASI change > 0.75). As shown in the heat map, a subset of responder patients (rightmost) had a strong down-regulation (blue) of most genes, indicating the potential to use these 17 genes to predict the response of patients to anti-IL-17 therapy. Down-regulation of these 17 genes indicated a better response to treatment, while up-regulation indicated a worse response.

The same analysis was performed prospective using patients receiving anti-IL-23 biologicals. This cohort was derived from patients enrolled in the STAMP trial by Mindera. Of this 17 patient cohort, 5 patients achieved a PASI-75 response 12 weeks ago, while 12 patients did not achieve this level of response. Cross-validation using regression modeling showed that using PASI 75 as a cutoff for evaluating classifier performance resulted in a balanced prediction accuracy of 0.70. Importantly, a positive predictive value of 1.00 was achieved. A total of 50 genes were ultimately determined to correlate with the patient's response to anti-IL-23 biotherapy (Table 13). TABLE 13 list of 50 genes identified as being associated with response to anti-IL 23 biologicals

/>

Disclosed herein is a platform utilizing a simple minimally invasive patch that obtains painless biomarker samples from psoriasis patients within a few minutes. The Mind. Px test enables large scale collection of patient data and when combined with high precision molecular testing will yield a powerful platform with excellent sensitivity and specificity. The use of this platform can potentially bring significant cost savings to the healthcare system, particularly when applied to predicting responses to expensive treatments-effectively eliminating the current trial-and-error approach to psoriasis treatment.

Biomarkers captured using Mind. Px include DNA, RNA, proteins, and small molecules. In particular, the role of RNA in chronic skin disease (well characterized) has been used to provide a predictive link between a patient's genetic markers and responsiveness to different drug classes. By capturing RNA from a patient's psoriasis lesions, more than 7000 biomarkers per test sample were evaluated using next generation sequencing. The results of this biomarker analysis may be used by healthcare providers and payors to predict the response of individual patients to a class of agents through the mechanism of action of that class of biopharmaceuticals to optimize treatment options. The use of Mind. Px can lead to better outcomes and significantly reduce the cost of the healthcare system.

Analysis of the classifier developed in this study showed high positive predictive value in assessing whether the patient would respond to the biopharmaceutical prior to initial exposure. In fact, in both classifiers, positive predictive values >90% can be achieved, which can significantly affect the medical practice and treatment of psoriasis patients. Interestingly, comparison of biomarker sets between algorithms showed some overlap (at least 2 gene overlaps between anti-IL-17 and anti-IL-23 classifiers).

Table 14. List of genes that can be used as classifiers to determine the effectiveness of one or more biological agents for treating psoriasis. In some embodiments, the methods disclosed herein relate to determining whether a subject suffering from psoriasis will produce a positive response to IL-17, IL-23, or tnfα mediated therapy, further comprising determining the presence, absence, or level of expression of at least 5 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence or expression level of at least 6 genes selected from table 14 to 17 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 6 genes selected from table 14 to 7 genes selected from table 14, 6 genes selected from table 14 to 8 genes selected from table 14, 6 genes selected from table 14 to 9 genes selected from table 14, 6 genes selected from table 14 to 10 genes selected from table 14, 6 genes selected from table 14 to 11 genes selected from table 14, 6 genes selected from table 14 to 12 genes selected from table 14, 6 genes selected from table 14 to 13 genes selected from table 14, 6 genes selected from table 14 to 14 genes selected from table 14, 6 genes selected from table 14 to 15 genes selected from table 14, 6 genes selected from table 14 to 16 genes selected from table 14, 6 genes selected from table 14 to 17 genes selected from table 14, 7 genes selected from table 14 to 8 genes selected from table 14, 7 genes selected from table 14 to 9 genes selected from table 14 7 genes selected from table 14 to 10 genes selected from table 14, 7 genes selected from table 14 to 11 genes selected from table 14, 7 genes selected from table 14 to 12 genes selected from table 14, 7 genes selected from table 14 to 13 genes selected from table 14, 7 genes selected from table 14 to 14 genes selected from table 14, 7 genes selected from table 14 to 15 genes selected from table 14, 7 genes selected from table 14 to 16 genes selected from table 14, 7 genes selected from table 14 to 17 genes selected from table 14, 8 genes selected from table 14 to 9 genes selected from table 14, 8 genes selected from table 14 to 10 genes selected from table 14, 8 genes selected from table 14 to 11 genes selected from table 14, 8 genes selected from table 14 to 12 genes selected from table 14, 8 genes selected from table 14 to 13 genes selected from table 14, 8 genes selected from table 14 to 14 genes selected from table 14, 8 genes selected from table 14 to 15 genes selected from table 14, 8 genes selected from table 14 to 16 genes selected from table 14, 8 genes selected from table 14 to 17 genes selected from table 14, 9 genes selected from table 14 to 10 genes selected from table 14, 9 genes selected from table 14 to 11 genes selected from table 14, 9 genes selected from table 14 to 12 genes selected from table 14, 9 genes selected from table 14 to 13 genes selected from table 14, 9 genes selected from table 14 to 14 genes selected from table 14, 9 genes selected from table 14 to 15 genes selected from table 14, 9 genes selected from table 14 to 16 genes selected from table 14, 9 genes selected from table 14 to 17 genes selected from table 14, 10 genes selected from table 14 to 11 genes selected from table 14 10 genes selected from table 14 to 12 genes selected from table 14, 10 genes selected from table 14 to 13 genes selected from table 14, 10 genes selected from table 14 to 14 genes selected from table 14, 10 genes selected from table 14 to 15 genes selected from table 14, 10 genes selected from table 14 to 16 genes selected from table 14, 10 genes selected from table 14 to 17 genes selected from table 14, 11 genes selected from table 14 to 12 genes selected from table 14, 11 genes selected from table 14 to 13 genes selected from table 14, 11 genes selected from table 14 to 14 genes selected from table 14, 11 genes selected from table 14 to 15 genes selected from table 14, 11 genes selected from table 14 to 16 genes selected from table 14, 11 genes selected from table 14 to 17 genes selected from table 14, 12 genes selected from table 14 to 13 genes selected from table 14, 12 genes selected from table 14 to 14 genes selected from table 14, 12 genes selected from table 14 to 15 genes selected from table 14, 12 genes selected from table 14 to 16 genes selected from table 14, 12 genes selected from table 14 to 17 genes selected from table 14, 13 genes selected from table 14 to 14 genes selected from table 14, 13 genes selected from table 14 to 15 genes selected from table 14, 13 genes selected from table 14 to 16 genes selected from table 14, 13 genes selected from table 14 to 17 genes selected from table 14, 14 genes selected from table 14 to 15 genes selected from table 14, 14 genes selected from table 14 to 16 genes selected from table 14, 14 genes selected from table 14 to 17 genes selected from table 14, 15 genes selected from table 14 to 16 genes selected from table 14, 15 genes selected from table 14 to 17 genes selected from table 14, or 16 genes selected from table 14 to 17 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 6 genes selected from table 14, 7 genes selected from table 14, 8 genes selected from table 14, 9 genes selected from table 14, 10 genes selected from table 14, 11 genes selected from table 14, 12 genes selected from table 14, 13 genes selected from table 14, 14 genes selected from table 14, 15 genes selected from table 14, 16 genes selected from table 14, or 17 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 6 genes selected from table 14, 7 genes selected from table 14, 8 genes selected from table 14, 9 genes selected from table 14, 10 genes selected from table 14, 11 genes selected from table 14, 12 genes selected from table 14, 13 genes selected from table 14, 14 genes selected from table 14, 15 genes selected from table 14, or 16 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 7 genes selected from table 14, 8 genes selected from table 14, 9 genes selected from table 14, 10 genes selected from table 14, 11 genes selected from table 14, 12 genes selected from table 14, 13 genes selected from table 14, 14 genes selected from table 14, 15 genes selected from table 14, 16 genes selected from table 14, or 17 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence or expression level of at least 20 genes selected from table 14 to 50 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 20 genes selected from table 14 to 25 genes selected from table 14, 20 genes selected from table 14 to 30 genes selected from table 14, 20 genes selected from table 14 to 35 genes selected from table 14, 20 genes selected from table 14 to 40 genes selected from table 14, 20 genes selected from table 14 to 45 genes selected from table 14, 20 genes selected from table 14 to 50 genes selected from table 14, 25 genes selected from table 14 to 30 genes selected from table 14, 25 genes selected from table 14 to 35 genes selected from table 14, 25 genes selected from table 14 to 40 genes selected from table 14, 25 genes selected from table 14 to 45 genes selected from table 14, 25 genes selected from table 14 to 50 genes selected from table 14, 30 genes selected from table 14 to 35 genes selected from table 14, 30 genes selected from table 14 to 40 genes selected from table 14, 30 genes selected from table 14 to 45 genes selected from table 14, 25 genes selected from table 14 to 30 genes selected from table 14, 35 genes selected from table 14 to 50 genes selected from table 14, 45 genes selected from table 14 to 50 genes selected from table 14, 50 genes selected from table 14 to 50 genes selected from table 14, table 14 to 50 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 20 genes selected from table 14, 25 genes selected from table 14, 30 genes selected from table 14, 35 genes selected from table 14, 40 genes selected from table 14, 45 genes selected from table 14, or 50 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 20 genes selected from table 14, 25 genes selected from table 14, 30 genes selected from table 14, 35 genes selected from table 14, 40 genes selected from table 14, or 45 genes selected from table 14. In some embodiments, the method further comprises determining the presence, absence, or level of expression of: at least 25 genes selected from table 14, 30 genes selected from table 14, 35 genes selected from table 14, 40 genes selected from table 14, 45 genes selected from table 14, or 50 genes selected from table 14.

/>

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. The present invention is not intended to be limited to the specific embodiments provided within this specification. While the invention has been described with reference to the above detailed description, the descriptions and illustrations of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the present invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

1. A microneedle device, comprising:

a. a microneedle zone comprising (i) a microneedle base substrate comprising a first base substrate surface and a second base substrate surface, wherein the first base substrate surface and the second base substrate surface are positioned on opposite sides of the microneedle base substrate; and (ii) a plurality of microneedles protruding from the first base substrate surface; and

b. A support substrate adjacent to the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate and having a support substrate depth, wherein the support substrate depth is greater than a minimum distance between the first base substrate surface and the second base substrate surface.

2. A microneedle device, comprising:

a. a microneedle zone comprising a microneedle base substrate comprising a first base substrate surface having a plurality of microneedles protruding from the first base substrate surface, the microneedle base substrate further comprising a second base substrate surface on an opposite side from the first base substrate surface, the second base substrate surface comprising grooves aligned with at least a portion of the plurality of microneedles; and

b. a support substrate adjacent to the microneedle base substrate, the support substrate being connected or integral with the microneedle base substrate.

3. The device of claim 1, wherein the minimum distance between the first and second base substrate surfaces of the microneedle base substrate is between about 1 μιη and about 500 μιη less than the support substrate depth.

4. The device of claim 1 or 2, wherein the minimum distance between the first base substrate surface and the second base substrate surface is between about 150 μιη to about 350 μιη.

5. The apparatus of claim 1, wherein a ratio between (a) the minimum distance between the first base substrate surface and the second base substrate surface and (b) the support substrate depth is at least 1:5.

6. The device of claim 1 or 2, wherein the microneedle area comprises a perimeter and the support base is adjacent to at least half of the perimeter.

7. The device of claim 1 or 2, wherein the support substrate comprises a first support substrate surface proximal to the plurality of microneedles and a second support substrate surface distal to the plurality of microneedles and positioned opposite the first support substrate surface, and wherein the second base substrate surface is non-coplanar with the second support substrate surface distal to the plurality of microneedles.

8. The apparatus of claim 1 or 2, wherein the first base substrate surface is non-coplanar with a surface of the support substrate.

9. The device of claim 1 or 2, wherein the plurality of microneedles are plasma treated.

10. The device of claim 1 or 2, wherein a plurality of probes are coupled to the microneedles of the plurality of microneedles.

11. The device of claim 9, wherein the plurality of probes comprises a negative charge.

12. The device of claim 1 or 2, wherein the plurality of microneedles comprise a polyolefin resin.

13. The device of claim 12, wherein the polyolefin resin comprises one or both of Zeonor1020R or Zeonor 690R.

14. The device of claim 1 or 2, wherein the microneedles of the plurality of microneedles are insoluble.

15. The device of claim 1 or 2, wherein the microneedles of the plurality of microneedles are pyramidal.

16. The device of claim 1 or 2, wherein the microneedles of the plurality of microneedles are solid.

17. The device of claim 15, wherein the angle between the base of the microneedle and the microneedle base is between about 60 ° and about 90 °.

18. The device of claim 2, wherein the grooves aligned with at least a portion of the plurality of microneedles have a width greater than a width of a mechanical applicator.

19. A kit comprising (a) the device of claim 2; and (b) a mechanical applicator that fits within the recess aligned with at least a portion of the plurality of microneedles.

20. The device of claim 1 or 2, wherein at least one of the plurality of microneedles is coupled to a nucleic acid probe.

21. The device of claim 20, wherein the nucleic acid probe comprises a homopolymer sequence.

22. The device of claim 21, wherein the homopolymer sequence comprises thymine or uracil.

23. The device of claim 20, wherein the nucleic acid probe comprises DNA.

24. The device of claim 20, wherein the nucleic acid probe comprises thymine.

25. The device of claim 20, wherein the nucleic acid probe comprises thymidine.

26. The device of claim 20, wherein the nucleic acid probe is covalently linked to the microneedle.

27. The device of claim 1 or 2, wherein the support substrate comprises fiducial markers.

28. The device of claim 1 or 2, wherein no more than three of the plurality of microneedles are less than 600 μιη in length or greater than 1050 μιη in length.

29. A method of preparing a biological sample from a subject, comprising:

a. contacting the skin of the subject with the microneedle device of any one of claims 1-29;

b. applying pressure to the microneedle device such that the microneedle device penetrates the skin of the subject; and

c. contacting nucleic acid within the skin of the subject with the microneedle device of any one of claims 1-28.

30. The method of claim 29, further comprising extracting the nucleic acid.

31. The method of claim 29 or 30, wherein the nucleic acid comprises RNA.

32. The method of claim 27 or 28, wherein the nucleic acid comprises mRNA.

33. The method of claim 30, further comprising converting the mRNA to cDNA.

34. The method of any one of claims 27-31, wherein the subject is a human.

35. The method of any one of claims 27-32, wherein the subject suffers from psoriasis or has symptoms of psoriasis.

36. The method of any one of claims 27-33, wherein the subject has a skin condition or has symptoms of a skin condition.

37. A method of preparing a biological sample from a subject, comprising:

a. contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises a plurality of nucleic acid probes coupled to a microneedle;

b. applying pressure to the microneedle device such that the microneedle device penetrates the skin of the subject;

c. allowing the microneedle device to penetrate the skin of the subject for no more than 10 minutes to obtain a ribonucleic acid (RNA) sample hybridized to the nucleic acid probe, wherein the RNA sample comprises a population of RNA fragments having about 700 bases or more and a population of RNA fragments having about 150 bases to about 200 bases, and wherein the ratio of the population of RNA fragments having about 700 bases or more to the population of RNA fragments having about 150 bases to about 200 bases is greater than 1; and

d. Removing the microneedle device from the skin of the subject, the microneedle device comprising the RNA sample hybridized to the nucleic acid probe after removing the microneedle device from the skin of the subject.

38. The method of claim 37, wherein the RNA sample is substantially free of contaminants.

39. The method of claim 37, comprising causing the microneedle device to penetrate the skin of the subject for about 5 minutes.

40. A method of treating autoimmune skin disease in a subject, comprising:

a. collecting a sample comprising RNA derived from skin from the subject, wherein the subject has not been administered an autoimmune therapeutic drug within 7 days prior to collecting the sample comprising RNA;

b. determining the expression level of at least one gene based on the RNA;

c. predicting that a subject suffering from said autoimmune skin disease will respond to said autoimmune therapeutic drug with a positive predictive value of greater than 80% based on said expression level of said at least one gene; and

d. treating the subject with the autoimmune therapeutic drug based on the prediction in (c).

41. The process as set forth in claim 40 wherein the PPV is greater than 90%.

42. The method of claim 40, wherein the PPV is greater than 95% for a cohort having more than 100 patients.

43. The method of claim 40, wherein the autoimmune therapeutic agent is a biologic.

44. The method of claim 40, wherein the autoimmune therapeutic agent comprises an antibody.

45. The method of claim 40, wherein the autoimmune therapeutic agent is an IL-17 mediated therapy, an IL-23 mediated therapy, or a TNFa mediated therapy.

46. The method of claim 40, wherein the autoimmune therapeutic agent is at least one agent selected from the group consisting of: etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan mab, budodamab, antique-in-c-lib mab, ti Qu Jizhu mab and Li Sanji mab.

47. The method of claim 40, wherein the at least one gene comprises at least five genes.

48. The method of claim 40, wherein the at least one gene comprises at least one gene from Table 6.

49. The method of claim 40, wherein the at least one gene comprises at least one gene from Table 12.

50. The method of claim 40, wherein the at least one gene comprises at least one gene from Table 13.

51. The method of claim 40, wherein the autoimmune disorder is psoriasis.

52. A method as in claim 40, wherein the object has a PASI of greater than 8.

53. The method of claim 40, wherein the subject has a PASI of at least 75 after treatment of the subject with the autoimmune therapeutic agent.

54. The method of claim 40, wherein the RNA comprises mRNA.

55. The method of claim 40, wherein the RNA comprises microRNA.

56. The method of claim 40, wherein collecting a sample comprising skin-derived RNA from the subject comprises penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises a microneedle conjugated with a nucleic acid probe.

57. The method of claim 40, further comprising converting the RNA to cDNA.

58. The method of claim 40, further comprising performing next generation sequencing of the cDNA.

59. The method of claim 40, further comprising applying a trained algorithm to data generated by the next generation sequencing of the cDNA.

60. The method of claim 40, wherein the at least one gene comprises at least two genes that do not share a common upstream regulator.

61. The method of claim 40, wherein the algorithm is trained using samples from patients administered a single type of drug selected from the group consisting of: IL-17 mediated therapy, TNF- α mediated therapy, and IL-23 mediated therapy.

62. The method of claim 40, wherein the autoimmune therapeutic agent is an IL-17 mediated therapy and the at least one gene comprises at least one gene that is not involved in an IL-17 mediated pathway.

63. The method of claim 40, wherein the autoimmune therapeutic agent is an IL-23 mediated therapy and the at least one gene comprises at least one gene that is not involved in an IL-23 mediated pathway.

64. The method of claim 40, wherein the autoimmune therapeutic agent is a TNF- α mediated therapy and the at least one gene comprises at least one gene that is not involved in a TNF- α mediated pathway.

65. A method of determining whether a subject's skin lesion will respond to an autoimmune therapeutic drug, comprising:

a. Collecting a sample comprising RNA derived from skin from the subject, wherein the subject has not administered the autoimmune therapeutic drug within 7 days prior to collecting the sample comprising RNA;

b. converting the RNA to cDNA;

c. determining the expression of at least one gene based on the cDNA; and

d. predicting with a positive predictive value of greater than 80% whether the subject with the skin lesion will respond to the autoimmune therapeutic drug.

66. The process of claim 65, wherein said PPV is greater than 90%.

67. The method of claim 65, wherein the PPV is greater than 95% for a cohort having more than 100 patients.

68. The method of claim 65, wherein the autoimmune therapeutic agent is a biologic.

69. The method of claim 65, wherein the autoimmune therapeutic agent comprises an antibody.

70. The method of claim 65, wherein the autoimmune therapeutic agent is an IL-17 mediated therapy, an IL-23 mediated therapy, or a tnfα mediated therapy.

71. The method of claim 65, wherein the autoimmune therapeutic agent is at least one agent selected from the group consisting of: etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, iral Bei Shan mab, budodamab, antique-in-c-lib mab, ti Qu Jizhu mab and Li Sanji mab.

72. The method of claim 65, wherein the autoimmune therapeutic agent is at least one agent selected from the group consisting of: cetuximab, wu Sinu mab, secukinumab, iral Bei Shan mab, brodamab, guluromab, ti Qu Jizhu mab and Li Sanji mab.

73. The method of claim 65, wherein the at least one gene comprises at least five genes.

74. The method of claim 65, wherein the at least one gene comprises at least one gene from Table 6.

75. The method of claim 65, wherein the at least one gene comprises at least one gene from Table 12.

76. The method of claim 65, wherein the at least one gene comprises at least one gene from Table 13.

77. The method of claim 65, wherein the autoimmune disorder is psoriasis.

78. A method as in claim 65, wherein the object has a PASI of greater than 8.

79. The method of claim 65, wherein the subject has a PASI of at least 75 after treatment of the subject with the autoimmune therapeutic agent.

80. The method of claim 65, wherein the RNA comprises mRNA.

81. The method of claim 65, wherein the RNA comprises microRNA.

82. The method of claim 65, wherein extracting RNA from the skin of the subject comprises penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises a microneedle conjugated with a nucleic acid probe.

83. The method of claim 65, further comprising performing next generation sequencing of the cDNA.

84. The method of claim 65, further comprising applying a trained algorithm to data generated by the next generation sequencing of the cDNA.

85. A method of determining whether a subject's skin lesion will respond to an autoimmune therapeutic drug, comprising:

a. penetrating the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a microneedle;

b. removing the microneedle device from the skin of the subject, thereby obtaining RNA molecules from the subject;

c. high throughput sequencing of the RNA molecules to generate sequence reads;

d. aligning the sequence reads with sequence read signatures associated with positive responses to autoimmune disease therapeutic drugs to obtain aligned sequence reads; and

e. Applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 80% for predicting a response to the autoimmune disease therapeutic drug.

86. A method of determining whether a subject's skin lesion will respond to an autoimmune therapeutic drug, comprising:

c. high throughput sequencing of the RNA molecules to generate sequence reads;

d. aligning the sequence reads with sequence read signatures associated with positive responses to autoimmune disease therapeutic drugs to obtain aligned sequence reads;

e. determining the expression level of at least one RNA molecule using the aligned sequence reads; and

f. applying a trained algorithm to the expression level of the at least one RNA molecule, wherein the trained algorithm predicts whether the subject with the skin lesion will respond to IL-17 mediated therapy, IL-23 mediated therapy, tnfa mediated therapy, or any combination thereof.

87. The method of claim 86, wherein the subject will respond to the IL-17 mediated therapy, the IL-23 mediated therapy, and the tnfa mediated therapy.

88. The method of claim 86, further comprising converting RNA to cDNA.

89. The method of claim 86, wherein said expression level of said at least one RNA molecule corresponds to expression of at least one gene from table 6.

90. The method of claim 86, wherein said expression level of said at least one RNA molecule corresponds to expression of at least one gene from table 12.

91. The method of claim 86, wherein said expression level of said at least one RNA molecule corresponds to expression of at least one gene from table 13.

92. The method of claim 86, wherein (b) further comprises:

i) High throughput sequencing the RNA biomarker to generate one or more sequence reads of the subject;

ii) aligning the one or more sequence reads of the subject with a known sequence read signature, wherein the known sequence read signature is associated with a positive response to the recommended treatment, thereby obtaining aligned sequence reads; and

iii) Classifying the subject as having a likelihood of producing a positive response to the recommended treatment by applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm has a positive predictive value of greater than 50% for predicting a positive response to the recommended treatment.

93. The method of claim 92 wherein the trained algorithm has a negative predictive value of greater than 50%.

94. The method of claim 92, wherein the recommended treatment comprises one or more autoimmune therapeutic drugs against an autoimmune disease or condition.

95. The method of claim 94, wherein the autoimmune disease or condition is psoriasis.

96. The method of claim 92, wherein the RNA biomarker is transcribed from at least one gene from table 6.

97. The method of claim 92, wherein the RNA biomarker is transcribed from at least one gene from table 12.

98. The method of claim 92, wherein the RNA biomarker is transcribed from at least one gene from table 13.

99. The method of claim 92, wherein the recommended treatment comprises etanercept, infliximab, adalimumab, cetuximab, wu Sinu mab, secukinumab, exemplar Bei Shan antibody, bromodamab, antique mab, tem Qu Jizhu mab, li Sanji bead mab, or any combination thereof.

100. The method of claim 92, wherein the one or more therapeutic agents comprise an autoimmune therapeutic agent against an autoimmune disease or condition.

101. The method of claim 100, wherein the autoimmune disease or condition is psoriasis.

102. A method of determining whether a subject suffering from an autoimmune skin disorder will respond to an autoimmune therapeutic agent, comprising:

a. extracting mRNA from the skin of the subject;

b. sequencing the mRNA from the skin of the subject; and

c. predicting with a positive predictive value greater than 80% whether the subject having the autoimmune disorder will respond to etanercept, adalimumab, infliximab, cetuximab, secukinumab, iximab Bei Shan, buddamab, coumarone, ti Qu Jizhu mab, and Li Sanji bead mab.

103. A method of determining whether a skin lesion of a subject will respond to IL-23 mediated therapy, comprising:

a. contacting the skin of the subject with a microneedle device, wherein the microneedle device comprises one or more nucleic acid probes coupled to a solid microneedle such that the microneedle device penetrates the skin of the subject;

c. high throughput sequencing of the RNA molecules to generate sequence reads; and

e. applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm predicts whether the subject with the skin lesion will respond to IL-23 mediated therapy, and the aligned sequence reads correspond to at least one gene from table 13.

104. A method of determining whether a subject's skin lesion will respond to IL-17, IL-23, or TNF- α mediated therapy, comprising:

e. applying a trained algorithm to the aligned sequence reads, wherein the trained algorithm predicts whether the subject with the skin lesion will respond to IL-17 mediated therapy, TNF- α mediated therapy, or IL-23 mediated therapy, and the aligned sequence reads correspond to at least one gene from table 6, table 12, or table 13.

105. The method of claim 104, further comprising treating the subject with TNF- α mediated therapy, wherein the aligned sequence reads correspond to at least one gene from the table 6.

106. The method of claim 104, further comprising treating the subject with an IL-17 mediated therapy, wherein the aligned sequence reads correspond to at least one gene from the table 12.

107. The method of claim 104, further comprising treating the subject with an IL-23 mediated therapy, wherein the aligned sequence reads correspond to at least one gene from the table 13.

108. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: CNFN, CTSC, GBAP1, CRABP2, PCDH7 and PPIG.

109. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: CNFN, CTSC, GBAP1, CRABP2, PCDH7 and PPIG.

110. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: PCDH7, PPIG, RAB31, C3 and EGR.

111. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes or four genes selected from the group consisting of: CNFN, CTSC, GBAP1 and CRABP2.

112. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes or four genes selected from the group consisting of: PPIG, RAB31, C3 and EGR.

113. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes or four genes selected from the group consisting of: PCDH7, PPIG, RAB31 and C3.

114. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes or four genes selected from the group consisting of: GBAP1, CRABP2, PCDH7 and PPIG.

115. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: KRT6A, SPRR1A, CD, IL4R, LCN2 and IFI27.

116. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: CD36, IL4R, S100A7A, SERPINB4, MX1 and SERPINB3.

117. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: LCN2, IFI27, DEFB4A, IL36G, CD, and PI3.

118. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: IL4R, LCN and IFI27.

119. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: PI3, IFI27 and SERPINB3.

120. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: IL4R, S A7A and MX1.

121. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: CD36, LCN2, and SERPINB4.

122. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: MTCO1P12, MTATP6P1, CLSTN1, PDPN, LDLRAD2, and GSTM3.

123. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: AL158847.1, DAD1, LDLRAD2, ZNF395, MGMT and AL136982.4.

124. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: NREP, PPIF, PRIM1, AL136982.5, MTATP6P1 and SMPD3.

125. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes, four genes, five genes, or six genes selected from: PDPN, TXNRD1, GSTM3, GPSM1, GLRX, and USP2.

126. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: MTCO1P12, CLSTN1 and GSTM3.

127. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: NREP, PPIF and PRIM1.

128. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: AL136982.5, MTATP6P1 and SMPD3.

129. The method of any one of claims 40, 65, 86, 92 and 104, wherein the at least one gene is three genes selected from the group consisting of: PDPN, TXNRD1, and GSTM3.